Validation of Smartphone-Based Heart Rate Variability Measurement Against ECG in Patients with Depression

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Comfort, Mario Müller, Erich Seifritz, Sebastian Olbrich This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7004304/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Heart rate variability (HRV) describes time fluctuations between consecutive heart beats, providing insight into the sympathetic and parasympathetic branches of the autonomic nervous system. In depressed patients, HRV has shown reduction due to autonomic dysregulation. Traditionally, its measurement is conducted using electrocardiography (ECG). A novel approach is measurement through a conventional smartphone via photoplethysmography (SPPG), which has not yet been explored in depressed patients. Thus, we developed PULSAR , an SPPG application which measures the user’s pulse wave using the smartphone camera and flash, without the need for external devices, to calculate HRV. To validate PULSAR , simultaneous 5-minute resting-state ECG and SPPG measurements were conducted in 15 healthy individuals and 15 depressed patients. In terms of analytical validation, the SPPG-derived HRV parameters demonstrated high correlations to their ECG- derived counterparts while systematically overestimating most variables. This overestimation is attributed to motion artifacts and the low sampling rate in SPPG measurements. Regarding clinical validation, HRV parameters in the depressed population were significantly reduced compared to healthy controls in both ECG and SPPG recordings. The frequency-domain parameter LF power, which primarily reflects sympathetic nervous system activity, emerged as the most robust variable for both clinical and analytical validation. Its SPPG-derived value showed a strong correlation with its ECG counterpart (r = 0.84) and a significant reduction in depressed patients (p = 0.021). Future iterations of the PULSAR application should focus on addressing its overestimation of HRV parameters, implementing longitudinal measurements in depressed patients, and potentially incorporating biofeedback techniques to introduce a therapeutic dimension. HRV SPPG smartphone depression biomarker ECG Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Introduction Heart Rate Variability and Depression Depression is among the most common illnesses, with an estimated 280 million affected people globally (WHO, 2023 ). The average lifetime prevalence of major depressive disorder is projected to be around 15% in high-income countries (Bromet et al., 2011 ). While traditionally being diagnosed clinically and by exclusion, the search for appropriate cost-effective, easy-access diagnostic and predictive biomarkers is an ongoing and challenging task (Arns et al., 2022 ). Heart Rate Variability (HRV) describes the time fluctuations in between consecutive heart beats. The heartbeat is regulated by the sinoatrial node, which in turn is influenced by the sympathetic and the parasympathetic nervous system (SNS and PNS) (Marek, 1996 ). Thus, HRV offers a non-invasive insight into the autonomic nervous system (ANS) whose main branches are the SNS and PNS. A decrease in HRV is associated with a predominant SNS activity (“fight or flight”), whereas an elevated HRV indicates increased PNS activity (“rest and digest”) (Siepmann et al., 2022 ). Different approaches of quantifying HRV have been established, whereas the time-domain and the frequency-domain parameters constitute the most common groups in addition to non-linear methods (Marek, 1996 ). Various time-domain indexes quantify the amount of variability in inter-beat intervals and can be used to interpret both the general ANS and the specific PNS activity. For calculating the frequency-domain, the fluctuations of the heartbeat signal are assigned to different frequency bands using spectral analysis. The high frequency (HF) band can be used to draw conclusions about PNS activity, while the low frequency (LF) band is an indicator of both SNS and PNS activity, with a predominance of SNS influence (Koch et al., 2019 ; Sgoifo et al., 2015 ). HRV is an active subject of research for psychiatry in general (Alvares et al., 2016 ; Quintana et al., 2016 ; Wang et al., 2023 ) and depression in particular (Kemp et al., 2010 ; Koch et al., 2019 ; Schiweck et al., 2019 ). It has been demonstrated that patients with major depression show multiple significantly reduced indexes of the time-domain as well as significantly reduced LF and HF parameters in the frequency-domain (Koch et al., 2019 ). The effects of altered SNS and PNS activity in depressed patients can be explained through the neurovisceral integration model, according to which emotional dysregulation is defined by interactions between cerebral affective systems and the ANS (Thayer & Lane, 2000 ). Although antidepressant medication seems to play a role in influencing HRV (Licht et al., 2010 ), its decrease in depressed patients appears to be independent of pharmacological treatments (Brown et al., 2018 ; Koch et al., 2019 ). Smartphone measurement of Heart Rate Variability The standard measurement method of obtaining HRV is electrocardiography (ECG) (Marek, 1996 ), which through high sampling rate and distinctive inter-beat intervals between R peaks allows for ideal measurement accuracy. An alternative, simpler way of detecting heart beats is photoplethysmography (PPG), which, in contrast, detects a pulse wave instead of an electrical signal (Allen, 2007 ). The usage of pulse rate variability (PRV) has largely been applied as a validated surrogate of HRV but is not without challenges (Mejía-Mejía et al., 2020 ; Schäfer & Vagedes, 2013 ). In psychiatric research in particular, PPG derived HRV has shown high diagnostical potential (Lyzwinski et al., 2023 ). The big advantage of PPG over ECG is its simplicity and widespread availability. Modern smartphones can turn into a PPG device by using the integrated flash as light source and the camera as photodetector (De Ridder et al., 2018 ; Jonathan & Leahy, 2010 ). The validity of approximating HRV by the use of smartphone PPG (SPPG) has been suggested in small-scale studies with healthy individuals (Holmes et al., 2020 ; Peng et al., 2015 ; Vondrasek et al., 2023 ). As previously stated, the topic of HRV in psychiatry is intensely researched, and today’s universal use of smartphones potentially offers a simple and cost-effective way of estimating a depressed patient’s HRV. Although suggested for psychophysiological research (Heathers, 2013 ; Liu et al., 2020 ), to this date, the promising idea of approximating a depressed patient’s HRV via SPPG alone has not yet been implemented. Thus, we developed PULSAR , an SPPG-based HRV application, and validated its performance in depressed patients and healthy individuals through simultaneous ECG-based HRV assessment. In this study, it was hypothesized that HRV parameters derived from SPPG correlate with those derived from ECG (analytical validation). SPPG-derived HRV parameters differ significantly between depressed patients and healthy individuals (clinical validation). Methods Design and participants We conducted a comparative, observational, cross-sectional validity study with 15 depressed patients and 15 healthy individuals. Depressed subjects (mean age 35.5 years, SD = 12.8 years, 9 males, 6 females) were recruited from the hospita’s specialized wards for depressive disorders. Inclusion criteria were age 18–65 years and a moderate to severe depressive episode following the criteria of ICD-10 as confirmed by a senior psychiatrist, with severity levels assessed by the Montgomery–Åsberg Depression Rating Scale (MADRS) (Montgomery & Asberg, 1979 ). MADRS is a ten-item questionnaire on depressive symptoms, assessed by a mental health professional. It yields a score from 0 to 60 and allows the classification of a depressive episode from mild to severe and has been described as the gold standard clinician rating scale for depression (Jauhar & Morrison, 2019 ). Exclusion criteria were any additional currently untreated major psychiatric conditions, any current non-psychiatric conditions, any forms of chronic cardiac arrhythmia or treatments interfering with ANS regulation of heartbeat. Healthy individuals (mean age = 35.7 years, SD = 13.6 years, 9 males, 6 females) were recruited from the private and professional network and matched precisely by age group and sex (See Table 1 ), being subjected to the same exclusion criteria. Table 1 Demographic characteristics of the study sample – categorized by age and gender Group Number Age Males Females Depressed 15 45 3 1 Healthy 15 45 3 1 This study was approved by the local ethics committee (Ethikkomission Zürich, BASEC ID 2023 − 01273). All participants provided written informed consent prior to participation, and their privacy rights were observed throughout the study. Measurements We conducted a short-term, 5-minute measurement of heart rate by ECG and smartphone simultaneously. Participants were resting in a 30° semi-recumbent position on an examination table and instructed to remain as immobile possible. A total of three consecutive measurements per participant was conducted to ascertain the measurement with the ideal conditions (absence of artifacts and noise in both measurement modalities). ECG For calculating ECG derived HRV, only the R peak intervals are relevant (Shaffer & Ginsberg, 2017 ). Therefore, we used the bipolar limb recordings as sufficient measurement modalities (Meek & Morris, 2002 ). For ECG measurement we used Kalamed KEC-1000®, which recorded the electrical signal with a sampling rate of 500 Hertz (Hz). ECG curves were plotted with the corresponding Kalamed ® software and exported in the CSV file format. We then chose the ECG channels with the most prominent R peak for HRV analysis visually. SPPG For recording the SPPG curve, we developed the Android ® application PULSAR . Upon launching the application, it assigns the participant a distinctive identifier and shows user instructions. The user’s index finger is placed firmly on the smartphone camera in a way that the finger fully covers the camera, and the smartphone flash illuminates the finger (see Fig. 1 ). During measurement, pulse peaks are detected in real-time and beats per minute are calculated and displayed. In addition, the user is given feedback on each detected pulse peak by slight vibration of the smartphone and a visual pulsating signal on the display. After the 5-minute measurement, HRV parameters are calculated (see HRV Analysis below) and displayed to the user and are ready for export for further analysis. PULSAR works by recording a video of the finger placed on the camera with a sampling rate of circa 30 Hz and adding up the amount of the red color in each video pixel (sum of red). Whenever a pulse wave passes the finger, the amount of red decreases due to the increased blood volume in some pixels, which results in a decrease of the sum of red in each video frame. Meanwhile, the smartphone flash illuminates the user’s finger and thereby accentuates the changes in red color. The SPPG pulse wave is therefore recorded by calculating the alterations of the color red over time (See Fig. 2 ) in the user’s index finger. Under ideal conditions, both the systolic pulse peaks and diastolic peaks can be distinctly recorded. The delay between the ECG’s R-peak (ventricular depolarization) and the pulse peak arises from the time required for the pulse wave to propagate from the heart to the fingertip. For pulse wave detection, we used pre-existing open-source code from Erdosi Balint’s HeartBeat project (Balint, 2020 ), which we adapted and expanded for the PULSAR application. We used the same Android ® phone (Realme C11 ®) for all measurements to ensure the same testing conditions for all participants. HRV Analysis Table 2 An overview of all Heart Rate Variability (HRV) parameters (Marek, 1996 ; Shaffer & Ginsberg, 2017 ) used in the current study as calculated by HeartPy Time-domain measures Parameter Unit Description SDNN ms Standard deviation of all NN intervals SDSD ms Standard deviation of differences between adjacent NN intervals. RMSSD ms Root mean square of successive RR interval differences PNN20 % Percentage of successive RR intervals that differ by more than 20 ms PNN50 % Percentage of successive RR intervals that differ by more than 50 ms Non-linear measures SD1 ms Poincaré plot standard deviation perpendicular the line of identity SD2 ms Poincaré plot standard deviation along the line of identity SD1/SD2 % Ratio of SD1-to-SD2 Frequency-domain measures VLF ms 2 Absolute power of the very-low-frequency band (0.0033–0.04 Hz) LF ms 2 Absolute power of the low-frequency band (0.04–0.15 Hz) HF ms 2 Absolute power of the high-frequency band (0.15–0.4 Hz) P_TOTAL ms 2 Variance of all NN intervals (0–0.4 Hz) LF_PERC % Relative power of the low-frequency band (0.04–0.15 Hz) HF_PERC % Relative power of the high-frequency band (0.15–0.4 Hz) LF_NU nu LF power in normalized units: LF / (P_TOTAL - VLF) * 100 HF_NU nu HF power in normalized units: HF / (P_TOTAL - VLF) * 100 HRV analysis was conducted using HeartPy (van Gent, 2018 ), an open-source Python library, previously validated for ECG and PPG assessment of HRV (van Gent et al., 2018 ; van Gent et al., 2019 ; Vićentić et al., 2022 ). Initially, ECG curves were cropped to match the exact measurement timespan of the 5-minute SPPG measurement. ECG and SPPG curves were analyzed using the process function of HeartPy , using Welch’s method (Welch, 1967 ) to extract the frequency spectrum for the frequency-domain. In some cases, SPPG curves showed some baseline wandering (Zhao et al., 2023 ), which posed a challenge to the calculation of peak-peak intervals. We applied HeartPy ’s filtering function remove_baseline_wander to correct the affected curves. In rarer instances, SPPG pulse peak amplitudes were not distinctive enough to obtain an accurate peak-peak interval. To solve this, the enhance_peaks function of HeartPy was applied . Statistical analysis For comparison between ECG measurements and Smartphone-based PULSAR analysis: Descriptive statistics were calculated to summarize the distributional aspects of both ECG and smartphone data. For each assessed HRV variable, the mean, median, standard deviation, and interquartile range were computed. A paired t-test was conducted for each variable to identify statistically significant differences between ECG and smartphone measurements. P-values were reported to assess the significance of these differences. Pearson correlation coefficients were used to quantify the linear association between the two methods and results were visualized in a heatmap to provide an overview of potential discrepancies. To visually inspect the relationship between measurements from both methods, scatterplots were created for each variable. To evaluate systematic deviations and biases between the two measurement methods, a Bland-Altman analysis was performed. This method plotted the mean of the two measurements against their difference for each variable, providing visual insight into agreement, systematic deviations, and outliers. Additionally, a statistical bias analysis was conducted to compare mean biases between healthy and depressed groups using t-tests to determine whether observed biases differed significantly. Comparison between patients with depression and healthy controls: To assess the validity of the smartphone method in distinguishing between healthy and depressed individuals and its agreement with ECG data, a comprehensive analysis was conducted. Descriptive statistics and mean comparisons were performed for each variable between healthy and depressed individuals, separately for each method (ECG and smartphone) to determine whether differences between the groups were statistically significant. To account for multiple comparisons, the Benjamin-Hochberg correction (FDR-BH) was applied, balancing Type-I and Type-II errors while maintaining statistical power. Given the high correlations between HRV parameters, a stricter Bonferroni correction would have been too conservative, potentially masking true group differences. FDH-BH retained the highest number of significant results, reinforcing the robustness of observed differences without inflating false positives. Z-scores were calculated for ECG and smartphone variables for standardized comparisons, and the results were illustrated using box-and-whisker plots for healthy and depressed individuals, providing a visual representation of group differences. Finally, to evaluate the predictive accuracy of both methods in distinguishing between depressed and healthy individuals, receiver operating characteristic (ROC) analyses were performed. AUC values, sensitivity, and specificity were calculated for each HRV variable. In our statistical analysis we omitted the VLF parameter since its measurement is discouraged in short-term (e.g. ≤ 5min) analysis of HRV. Additionally, the HF_NU parameter was excluded as it is mathematically complementary to the LF_NU parameter (Marek, 1996 ), resulting in identical correlations and providing no additional analytical value. Results Comparison of smartphone and ECG results Smartphone derived HRV values yielded higher mean values for most HRV parameters, exhibiting significant differences (see descriptive results in Table 3 ). However, moderate to high correlations were found for most variables (e.g. BPM, P_TOTAL, LF), indicating a strong methodological agreement between the two measurement methods (see heatmap in Fig. 3 for further details). In contrast, variables with lower associations, such as SD1/SD2 or LF_PERC, indicate particular inconsistencies of results for the different methods of assessment. Table 3 Descriptive statistics of and associations between the ECG- and smartphone-derived HRV parameters Variable ECG Mean ECG SD ECG Median (IQR) Smartphone Mean Smartphone SD Smartphone Median (IQR) t-test p-value Correlation BPM 74.94 13.44 69.87 (64.20; 84.53) 75.25 13.12 70.06 (64.90; 84.40) 0.095 0.997 SDNN 46.75 21.88 41.57 (30.13; 67.06) 66.28 22.43 60.28 (47.90; 82.05) 0.000 0.764 SDSD 23.85 17.58 19.31 (10.55; 28.41) 47.08 13.84 43.71 (34.58; 54.69) 0.000 0.528 RMSSD 35.38 23.70 31.55 (16.36; 36.57) 67.42 18.38 64.45 (51.84; 80.86) 0.000 0.695 PNN20 0.39 0.24 0.41 (0.17; 0.55) 0.75 0.06 0.75 (0.69; 0.79) 0.000 0.791 PNN50 0.12 0.15 0.07 (0.01; 0.12) 0.37 0.11 0.35 (0.27; 0.44) 0.000 0.757 SD1 25.01 16.75 22.31 (11.57; 25.85) 47.47 12.77 45.56 (36.66; 57.14) 0.000 0.682 SD2 59.96 27.29 55.12 (41.08; 82.35) 69.40 29.52 63.64 (49.81; 85.39) 0.006 0.817 SD1/SD2 0.41 0.16 0.36 (0.29; 0.51) 0.76 0.24 0.74 (0.60; 1.01) 0.000 0.123 LF 852.88 783.81 465.33 (186.68; 1608.14) 935.12 727.58 736.28 (330.93; 1432.01) 0.302 0.842 HF 601.92 797.59 279.64 (111.63; 813.59) 1243.06 992.14 1014.77 (542.54; 1539.74) 0.000 0.893 P_TOTAL 1454.81 1469.87 793.00 (335.90; 2388.71) 2178.18 1587.28 1873.30 (1119.03; 2938.36) 0.000 0.908 LF_PERC 37.63 11.94 39.09 (27.44; 47.15) 32.80 11.32 33.02 (24.05; 40.36) 0.072 0.261 HF_PERC 23.19 12.76 21.25 (13.41; 30.83) 46.23 14.05 47.25 (34.92; 56.82) 0.000 0.309 LF_NU 62.76 15.80 66.48 (56.59; 73.80) 41.97 14.22 41.69 (28.10; 52.84) 0.000 0.314 Accordingly, scatterplots in Fig. 4 demonstrate a clear alignment along the trendlines, indicating a strong overall agreement between the two methods. For variables such as BPM, LF, or P_TOTAL, data points closely follow the line of best fit, suggesting consistent measurement performance. Systematic deviations are observed in variables such as SDNN, SDSD, PNN20, or SD1, where smartphone measurements tend to be slightly higher at lower ECG values but converge and align at higher levels. In contrast, variables such as SD1/SD2 and LF_PERC exhibit a greater degree of scatter, indicating potential measurement variability while still suggesting an underlying agreement trend. Similarly, the Bland-Altman analyses in Fig. 5 demonstrate an overall strong agreement between the two methods for most variables. Variables such as BPM, SDNN, and SD2 show differences that are consistently centered close to the zero line, with only a few outliers outside the 95% limits of agreement. In contrast, systematic deviations are observed for variables such as PNN20, SD1, RMSSD, and SDSD, where smartphone values tend to be higher at lower mean levels but align more closely with ECG measurements as the means increase. These patterns suggest a systematic (and thus predictable) shift, which further supports the trends identified in the scatterplots. The bias analysis confirms a consistent level of agreement between the ECG and smartphone measurements across healthy and depressed groups for most variables (see Table 4 ). A clear exception is SD1/SD2, where a significant group-specific difference in bias suggests a systematic deviation. Additionally, SDSD and PNN20 variables are close to significance, suggesting trends toward group-specific bias that warrant further investigation. For most variables, including BPM, SDNN, LF, HF, and P_TOTAL, the mean biases remain comparable across groups, underscoring the robustness of both measurement methods. These findings complement the Bland-Altman results, supporting the reliability of the smartphone as a viable alternative to ECG for most HRV parameters. Table 4 Bias analysis comparing ECG- and smartphone-derived HRV parameters in healthy and depressed individuals. Variable Healthy Mean Bias Healthy SD Depressed Mean Bias Depressed SD t-statistic p-value BPM -0.59 1.27 -0.05 0.58 -1.51 0.143 SDNN -17.42 17.05 -21.64 13.39 0.75 0.457 SDSD -17.71 13.39 -28.76 16.14 2.04 0.051 RMSSD -26.78 13.06 -37.3 19.47 1.74 0.093 PNN20 -0.29 0.15 -0.42 0.22 1.88 0.07 PNN50 -0.24 0.1 -0.25 0.1 0.1 0.924 SD1 -18.57 9.62 -26.34 13.77 1.79 0.084 SD2 -8.52 22.65 -10.35 10.33 0.28 0.778 SD1/SD2 -0.22 0.22 -0.47 0.27 2.77 0.01 LF -31.02 568.95 -133.45 226.58 0.65 0.522 HF -691.12 358.25 -591.16 542.71 -0.6 0.556 P_TOTAL -722.13 651.14 -724.61 704.62 0.01 0.992 LF_PERC 0.91 13.58 8.75 14.05 -1.55 0.131 HF_PERC -19.64 8.98 -26.45 20.28 1.19 0.244 LF_NU 17.26 11.54 24.34 22 -1.1 0.279 Comparison of healthy and depressed population The results of the group comparisons indicate that ECG-based HRV variables effectively differentiate between depressed and healthy individuals, with smartphone measurements largely replicating these findings (See Table 5 , Figs. 6 a & 6 b). To control for multiple comparisons, p-values were adjusted using the FDR-BH correction, ensuring a balanced approach between Type-I and Type-II errors. BPM and LF show strong agreement between methods, with similar means and consistent group differences, indicating their reliability in detecting depressive states. Systematic shifts were observed for SDNN, SD2, and P_TOTAL, where smartphone measurements tended to be higher than ECG values, yet group differences remained comparable between the two assessment methods. These differences remained statistically significant after correction, supporting the robustness of these HRV markers. In contrast, SD1, RMSSD, HF, and PNN20 did not remain significant after correction, indicating limited discriminatory power. Table 5 Descriptive statistics of and comparison of HRV parameters between healthy and depressed individuals for both ECG- and smartphone-derived measurements ECG Smartphone Variable Mean Healthy ± SD Mean Depressed ± SD Difference p-value Adj. p-value (FDR-BH) Mean Healthy ± SD Mean Depressed ± SD Difference p-value Adj. p-value (FDR-BH) BPM 68.23 ± 8.20 81.65 ± 14.51 -13.42 0.004 0.008 68.82 ± 8.09 81.69 ± 14.21 -12.88 0.005 0.021 SDNN 59.52 ± 21.00 33.98 ± 14.19 25.54 0.001 0.008 76.94 ± 23.40 55.62 ± 15.86 21.32 0.007 0.021 SDSD 33.28 ± 20.06 14.43 ± 6.88 18.85 0.002 0.008 50.99 ± 13.66 43.18 ± 13.32 7.80 0.125 0.144 RMSSD 47.34 ± 26.06 23.42 ± 13.32 23.92 0.004 0.008 74.12 ± 19.03 60.72 ± 15.55 13.40 0.044 0.072 PNN20 0.49 ± 0.21 0.30 ± 0.25 0.18 0.037 0.056 0.77 ± 0.06 0.72 ± 0.05 0.05 0.017 0.036 PNN50 0.18 ± 0.18 0.07 ± 0.11 0.11 0.053 0.072 0.42 ± 0.11 0.32 ± 0.09 0.11 0.007 0.021 SD1 33.47 ± 18.43 16.56 ± 9.42 16.91 0.004 0.008 52.04 ± 13.14 42.89 ± 10.98 9.15 0.048 0.072 SD2 75.35 ± 26.18 44.57 ± 18.70 30.78 0.001 0.008 83.87 ± 31.42 54.92 ± 19.22 28.95 0.005 0.021 SD1/SD2 0.45 ± 0.20 0.37 ± 0.11 0.08 0.172 0.215 0.68 ± 0.20 0.84 ± 0.25 -0.16 0.056 0.076 LF 1249.94 ± 788.39 455.83 ± 559.66 794.10 0.004 0.008 1280.95 ± 727.36 589.28 ± 557.85 691.67 0.007 0.021 HF 934.90 ± 993.73 268.94 ± 304.39 665.96 0.019 0.032 1626.02 ± 1165.27 860.11 ± 605.71 765.91 0.032 0.060 P_TOTAL 2184.84 ± 1636.92 724.78 ± 808.59 1460.06 0.004 0.008 2906.97 ± 1736.22 1449.39 ± 1032.60 1457.59 0.009 0.023 LF_PERC 35.65 ± 10.04 39.61 ± 13.64 -3.97 0.372 0.429 34.74 ± 12.43 30.86 ± 10.15 3.87 0.358 0.358 HF_PERC 22.40 ± 11.55 23.97 ± 14.23 -1.57 0.743 0.796 42.04 ± 12.18 50.42 ± 14.94 -8.38 0.103 0.129 LF_NU 62.39 ± 14.50 63.14 ± 17.51 -0.75 0.899 0.899 45.13 ± 13.53 38.80 ± 14.65 6.33 0.229 0.245 Building on these findings, we assessed the predictive accuracy of both methods by analyzing AUC values, sensitivity, and specificity for each variable. (See Table 6 ). Overall, AUC analysis shows high AUC values and moderate- to high sensitivity and specificity across time- and frequency domain HRV variables in both ECG and smartphone measurements, suggesting a strong discriminatory power for distinguishing between our healthy and depressed population. However, some variables, such as LF_PERC, HF_PERC and LF_NU showed inconsistent results. While the AUC values for these metrics are moderate in some cases, their overall discriminative ability (and thus clinical utility) remains limited due to minimal and statistically non-significant group differences observed earlier. Notably, SD1/SD2 emerges as a variable where smartphone measurements outperform ECG by means of group differentiation, underscoring the smartphone’s potential competitive performance for selected metrics. Table 6 Area under the curve (AUC), sensitivity and specificity values of ECG- and smartphone-derived HRV parameters for distinguishing between healthy and depressed individuals ECG Smartphone Variable AUC Sensitivity Specificity AUC Sensitivity Specificity BPM 0.78 0.6 0.93 0.78 0.6 0.93 SDNN 0.84 0.8 0.73 0.8 0.73 0.8 SDSD 0.86 0.93 0.67 0.66 0.6 0.8 RMSSD 0.83 0.53 1 0.7 0.6 0.8 PNN20 0.71 0.53 0.93 0.76 0.8 0.73 PNN50 0.74 0.67 0.8 0.76 0.8 0.73 SD1 0.83 0.53 1 0.69 0.6 0.8 SD2 0.83 0.8 0.73 0.8 0.8 0.73 SD1/SD2 0.59 1 0.27 0.7 0.6 0.87 LF 0.8 0.87 0.67 0.81 0.87 0.8 HF 0.76 0.6 0.87 0.73 0.53 0.93 P_TOTAL 0.79 0.87 0.67 0.78 0.47 1 LF_PERC 0.61 0.6 0.73 0.59 0.8 0.53 HF_PERC 0.5 0.6 0.6 0.69 0.67 0.8 LF_NU 0.55 0.67 0.6 0.64 0.33 0.93 Discussion This study presents a novel modality for approximating reduced HRV in depressed patients using only a standard smartphone without external devices, validating it against the state-of-the-art assessment of HRV via ECG. We confirmed Hypothesis 1 by demonstrating that SPPG-derived HRV parameters are highly correlated to their ECG based counterparts during simultaneous 5-minute resting measurements in patients with depression and healthy individuals. Additionally, our depressed population exhibited a significant reduction in HRV parameters across time- and frequency domain measures in both ECG and SPPG recordings compared to the healthy group, confirming Hypothesis 2, While previous studies have either extensively examined altered HRV in depression or the usage of (S)PPG as a surrogate of the gold standard ECG, to the author’s knowledge, this is the first application of a smartphone-only approach in clinically depressed patients. Previous studies have demonstrated strong correlations between ECG- and SPPG derived HRV parameters across time- and frequency domain measures (Królak et al., 2020 ; Vondrasek et al., 2023 ; Zhao et al., 2023 ). Regarding analytical validation, our findings replicated these results for BPM and the time- and frequency domain parameters with one exception in each case. However, SPPG measurement tended to overestimate most HRV variables, particularly at lower values, leading to significant differences between both measurement methods. An exception to this trend was the LF power parameter, which exhibited both a high correlation and a non-significant difference between SPPG and ECG. The robustness of this parameter was confirmed by additional analysis (Bland-Altman and bias analysis). We partly attribute this systematic overestimation to motion artifacts during our SPPG measurements. Involuntary finger movement during (S)PPG measurement can introduce gaps and falsely detected pulse peaks, significantly altering HRV results (Królak et al., 2020 ). Additionally, we link the overestimation to PULSAR ’s relatively low sampling rate of 30 Hz. Previous studies have shown a systematic overestimation of PPG-derived HRV parameters at lower sampling rates, with the magnitude of overestimation increasing as the sampling rate decreases (Burma et al., 2024 ; Pelaez-Coca et al., 2022 ). This effect is attributed to a decrease in distinction of the pulse peak in lower sampling rates, leading to reduced precision in quantifying the interbeat intervals (Burma et al., 2024 ). To accurately quantify HRV parameters using PULSAR , calibration algorithms to correct for overestimation need to be implemented in the application. Nevertheless, current results remain sufficiently reliable for intra- and interindividual comparison of SPPG derived HRV parameters. Recent systematic reviews and meta-analyses have consistently shown significant increases in BPM and decreases in HRV parameters across time- and frequency domain measures in depressed patients (Koch et al., 2019 ; Schiweck et al., 2019 ; Burma et al., 2024 ). In terms of clinical validation, our study fully corroborates these findings through ECG-derived parameters and validates them for SPPG variables except for SDSD. This not only confirms previous research highlighting reduced HRV in depression but more importantly underscores the potential of SPPG in measuring said reduced HRV. Notably, the LF power parameter stood out again due to its strong performance: It exhibited a significant reduction in descriptive group comparisons (p = 0.021), a narrow interquartile range with minimal overlap in the box-and-whisker plots, and robust diagnostic accuracy in the ROC analysis for detecting depression (AUC = 0.81, sensitivity = 0.87, specificity = 0.80). The reported literature overlooks the non-linear parameters (SD1, SD2) as well as LF_PERC, HF_PERC and LF_NU of the frequency domain, suggesting a secondary importance of those parameters for stratifying depression. These findings prompted a closer examination of the frequency domain of HRV. ANS influences lead to a fluctuating heart rate signal over time. Through spectral analysis, these oscillatory components can be broken down into different frequency bands (Malliani et al., 1991 ), with the LF band being set at 0.03–0.15 Hz and the HF band at 0.15–0.4 Hz (Eckberg, 1997 ). The LF band is proposed to be influenced by both PNS and SNS activity, whereas SNS activation appears to play a dominant role in its increase. The HF band is considered to be solely influenced by PNS activity (Sgoifo et al., 2015 ). In our depressed population, both LF and HF power parameters showed significant decreases, which suggests reduced activity of SNS as well as PNS branches of the ANS in depressed patients. Given LF power's robustness as the most reliable variable measured by the PULSAR application, SPPG-derived measurements of this parameter can be proposed as a potential biomarker for gaining meaningful insights into the PNS and SNS activity of depressed individuals. HRV has been suggested as a viable diagnostic biomarker for mental disorders in general (Beauchaine & Thayer, 2015 ; Tomasi et al., 2024 ; Villar de Araujo et al., 2023) and depression in particular (Gullett et al., 2023 ; Mulcahy et al., 2019 ; Schiweck et al., 2019 ). Patients with recent suicide attempts have exhibited significant reduction in HRV parameters across time- and frequency domain measures (Rüesch et al., 2023 ). Beyond its diagnostic utility, heart rate analysis has demonstrated predictive potential for antidepressant medication with serotonin-norepinephrine reuptake inhibitors (SNRIs) (Olbrich et al., 2016 ). Furthermore, a recent study utilized HRV parameters to discriminate between depressive responders and non-responders to ketamine therapy, underscoring its potential as a predictive biomarker for treatment outcomes (Meyer et al., 2021 ). Given that SPPG measurement of HRV offers a non-invasive, easy-access and cost-effective modality (De Ridder et al., 2018 ), its potential in detecting, stratifying and analysis of the predictiveness of HRV-derived biomarkers merits significant consideration. Strengths and Limitations We investigated a total of 14 common HRV parameters in addition to beats per minute (BPM) and conducted rigorous descriptive and visual analyses. This allowed us to perform a broad analytical and clinical validation process and ascertain the specific SPPG derived HRV parameters with the highest (e.g. LF power) and the least (e.g. SDSD) potential. For this study, we used a relatively small dataset of n = 30 participants. While this sample size was deemed appropriate for an initial feasibility study in this research area, future studies should include a larger cohort to strengthen our findings. Identical measurement conditions (same smartphone, reclining position, finger placement, room lighting) were maintained for all participants. In practical applications, however, PULSAR would be used in a variety of settings where external factors, particularly lighting conditions, could affect measurement precision. Additionally, PULSAR’s SPPG measurements seem to be susceptible to movement artifacts, which can distort the recorded pulse wave and the derived HRV parameters. To mitigate this, we selected the result least affected by artifacts from three measurements and applied HeartPy’s filtering functions. For future iterations, we aim to implement detection and correction algorithms into PULSAR to automatically reduce movement artifacts. Future research should focus on longitudinally assessing HRV parameters in depressed patients using PULSAR , with particular emphasis on the LF power parameter. Such studies would not only reinforce our findings but also provide patients with meaningful insights into their ANS by offering a user-friendly, HRV-derived score. Numerous studies have demonstrated the efficacy of HRV biofeedback training (Goessl et al., 2017 ; Laborde et al., 2022 ). Future iterations of our smartphone application could integrate biofeedback techniques, such as breathing exercises (Steffen et al., 2021 ), adding a therapeutic dimension to PULSAR . Conclusion In terms of analytical validation, our study demonstrated strong to very strong correlations between most SPPG-derived HRV parameters and their ECG counterparts. However, SPPG measurements exhibited systematic overestimations, particularly at lower HRV values. We attribute this to motion artifacts and reduced sampling rate. Regarding clinical validation, our depressed population revealed a significant reduction in HRV measured by SPPG- and confirmed by ECG-based measurements. Among the assessed variables, the frequency-domain LF power parameter (r = 0.84, p = 0.021, AUC = 0.81, sensitivity = 0.87, specificity = 0.80), which is chiefly dependent on sympathetic nervous system activity, emerged as the most reliable metric, demonstrating strong potential for detecting HRV reductions in depressed patients. These findings support the theory of autonomic dysregulation with altered sympathetic and reduced parasympathetic nervous system activity in depression. In conclusion, our study demonstrated the feasibility of approximating HRV reduction in depression using SPPG alone, without the need for external devices. This further reinforces the role of HRV as a biomarker in depression and highlights smartphone-based measurement as an accessible and cost-effective modality for its acquisition. Future studies should aim to address the systematic overestimations observed in SPPG measurements, conduct longitudinal assessment of HRV in larger groups of depressed patients and incorporate biofeedback techniques into our smartphone application. Declarations Funding This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors. Open access funding for this publication was provided by the University of Zurich. Competing interests The authors declare no competing interests. Data Availability Data will be made available upon reasonable request to the corresponding author. Ethical Approval This study was approved by the local ethics committee (Ethikkomission Zürich, BASEC ID 2023-01273) on November 23, 2023. Informed Consent All participants provided written informed consent prior to participation, and their privacy rights were observed throughout the study. CRediT authorship contribution statement Lion D. 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Retrieved June 29, 2025, from https://www.who.int/news-room/fact-sheets/detail/depression Zhao, H., Li, T., Yang, J., & Pang, C. (2023). An error-bounded median filter for correcting ECG baseline wander. Health Inf Sci Syst , 11 (1), 45. https://doi.org/10.1007/s13755-023-00235-w Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7004304","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":485677690,"identity":"f3f65791-882a-432b-8b35-734963a0edc0","order_by":0,"name":"Lion D. Comfort","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA1klEQVRIiWNgGAWjYJCCD0Asw8bAwPgAyODhI0IH4wwGBgMeoBZmA5AWNqK1ABlsEiAuQS387d2JDR/3/OHhY+89Vvk1xw7oQuaHj27g0SJx5uzGxhnPgA7jOZd2W3ZbMtBhbMbGOXi0GEjkbn/McwCoRSLH7LbkNmagFh42aQJaNjbDtBRLbqsnUQvjx22HCWuB+OWAMdAvZ4ylGbcd52FjJuAX/vbejQ0fDsjJybf3GH78ua3anp+9+eFjfFpQADMPmCRWOQgw/iBF9SgYBaNgFIwYAACQhEAx7vzuTwAAAABJRU5ErkJggg==","orcid":"","institution":"University Hospital of Psychiatry Zurich","correspondingAuthor":true,"prefix":"","firstName":"Lion","middleName":"D.","lastName":"Comfort","suffix":""},{"id":485677691,"identity":"71fa0442-de0b-4cb3-b6ff-e68d09b31e5b","order_by":1,"name":"Mario Müller","email":"","orcid":"","institution":"University Hospital of Psychiatry Zurich","correspondingAuthor":false,"prefix":"","firstName":"Mario","middleName":"","lastName":"Müller","suffix":""},{"id":485677692,"identity":"92a13169-264d-4308-a80b-1db798b2658e","order_by":2,"name":"Erich Seifritz","email":"","orcid":"","institution":"University Hospital of Psychiatry Zurich","correspondingAuthor":false,"prefix":"","firstName":"Erich","middleName":"","lastName":"Seifritz","suffix":""},{"id":485677693,"identity":"95651014-0ad1-447c-b50c-23e891c1a992","order_by":3,"name":"Sebastian Olbrich","email":"","orcid":"","institution":"University Hospital of Psychiatry Zurich","correspondingAuthor":false,"prefix":"","firstName":"Sebastian","middleName":"","lastName":"Olbrich","suffix":""}],"badges":[],"createdAt":"2025-06-29 18:08:08","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7004304/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7004304/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":87318547,"identity":"3e1da5d3-124b-4641-91ad-df6fd1ec218d","added_by":"auto","created_at":"2025-07-22 16:16:25","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":3325918,"visible":true,"origin":"","legend":"\u003cp\u003ePULSAR in use. The user places his index finger on the smartphone camera while the flash illuminates the finger\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-7004304/v1/383e4e75b4178ed2773431df.png"},{"id":87318544,"identity":"fa0dd692-a9b6-4984-aa7c-5ac53aa8a600","added_by":"auto","created_at":"2025-07-22 16:16:25","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":112676,"visible":true,"origin":"","legend":"\u003cp\u003eExemplary PULSAR recording over 10 seconds (red) mapped over simultaneous ECG recording (orange)\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-7004304/v1/6bb879a278ef2264fe636b21.png"},{"id":87318543,"identity":"5c35a2c1-9750-42b0-91e1-35920f539427","added_by":"auto","created_at":"2025-07-22 16:16:25","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":44588,"visible":true,"origin":"","legend":"\u003cp\u003eHeatmap of correlations between ECG- and smartphone-derived HRV parameters\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-7004304/v1/3700a2df1eeac313c56af121.png"},{"id":87318551,"identity":"c41b9971-1c86-4480-98db-bf52500c88e7","added_by":"auto","created_at":"2025-07-22 16:16:25","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":211897,"visible":true,"origin":"","legend":"\u003cp\u003eScatterplots of ECG- and smartphone-derived HRV parameters with fitted regression lines\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-7004304/v1/57fbb956e9b16a1bff572f1c.png"},{"id":87318545,"identity":"0dad7fec-f868-4766-88b8-171538e8a0bd","added_by":"auto","created_at":"2025-07-22 16:16:25","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":247786,"visible":true,"origin":"","legend":"\u003cp\u003eBland-Altman plots analyzing the agreement between ECG- and smartphone-derived HRV measurements\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-7004304/v1/ed28816b2d963983341b861f.png"},{"id":87320303,"identity":"22d79af6-cba3-427b-8f94-f98e7c918851","added_by":"auto","created_at":"2025-07-22 16:24:25","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":701568,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ea \u0026amp; b\u003c/strong\u003e Box-and-whisker plot of Z-scores for ECG- (Fig. 6a) and smartphone- (Fig. 6b) derived HRV parameters, comparing depressed (blue) and healthy (green) individuals\u003c/p\u003e","description":"","filename":"floatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-7004304/v1/e775a6c96edd0795f6aace07.png"},{"id":96245688,"identity":"bc9ad27a-62ee-4a43-a39a-3219a17945c4","added_by":"auto","created_at":"2025-11-19 07:21:55","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":8180196,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7004304/v1/88775d6d-6882-415f-8a1b-07d75ee414d2.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Validation of Smartphone-Based Heart Rate Variability Measurement Against ECG in Patients with Depression","fulltext":[{"header":"Introduction","content":"\u003cp\u003eHeart Rate Variability and Depression\u003c/p\u003e\u003cp\u003eDepression is among the most common illnesses, with an estimated 280\u0026nbsp;million affected people globally (WHO, \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). The average lifetime prevalence of major depressive disorder is projected to be around 15% in high-income countries (Bromet et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). While traditionally being diagnosed clinically and by exclusion, the search for appropriate cost-effective, easy-access diagnostic and predictive biomarkers is an ongoing and challenging task (Arns et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eHeart Rate Variability (HRV) describes the time fluctuations in between consecutive heart beats. The heartbeat is regulated by the sinoatrial node, which in turn is influenced by the sympathetic and the parasympathetic nervous system (SNS and PNS) (Marek, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e1996\u003c/span\u003e). Thus, HRV offers a non-invasive insight into the autonomic nervous system (ANS) whose main branches are the SNS and PNS. A decrease in HRV is associated with a predominant SNS activity (“fight or flight”), whereas an elevated HRV indicates increased PNS activity (“rest and digest”) (Siepmann et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Different approaches of quantifying HRV have been established, whereas the time-domain and the frequency-domain parameters constitute the most common groups in addition to non-linear methods (Marek, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e1996\u003c/span\u003e). Various time-domain indexes quantify the amount of variability in inter-beat intervals and can be used to interpret both the general ANS and the specific PNS activity. For calculating the frequency-domain, the fluctuations of the heartbeat signal are assigned to different frequency bands using spectral analysis. The high frequency (HF) band can be used to draw conclusions about PNS activity, while the low frequency (LF) band is an indicator of both SNS and PNS activity, with a predominance of SNS influence (Koch et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Sgoifo et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2015\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eHRV is an active subject of research for psychiatry in general (Alvares et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Quintana et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Wang et al., \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) and depression in particular (Kemp et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Koch et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Schiweck et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). It has been demonstrated that patients with major depression show multiple significantly reduced indexes of the time-domain as well as significantly reduced LF and HF parameters in the frequency-domain (Koch et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). The effects of altered SNS and PNS activity in depressed patients can be explained through the neurovisceral integration model, according to which emotional dysregulation is defined by interactions between cerebral affective systems and the ANS (Thayer \u0026amp; Lane, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2000\u003c/span\u003e). Although antidepressant medication seems to play a role in influencing HRV (Licht et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2010\u003c/span\u003e), its decrease in depressed patients appears to be independent of pharmacological treatments (Brown et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Koch et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eSmartphone measurement of Heart Rate Variability\u003c/p\u003e\u003cp\u003eThe standard measurement method of obtaining HRV is electrocardiography (ECG) (Marek, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e1996\u003c/span\u003e), which through high sampling rate and distinctive inter-beat intervals between R peaks allows for ideal measurement accuracy. An alternative, simpler way of detecting heart beats is photoplethysmography (PPG), which, in contrast, detects a pulse wave instead of an electrical signal (Allen, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). The usage of pulse rate variability (PRV) has largely been applied as a validated surrogate of HRV but is not without challenges (Mejía-Mejía et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Schäfer \u0026amp; Vagedes, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). In psychiatric research in particular, PPG derived HRV has shown high diagnostical potential (Lyzwinski et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). The big advantage of PPG over ECG is its simplicity and widespread availability. Modern smartphones can turn into a PPG device by using the integrated flash as light source and the camera as photodetector (De Ridder et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Jonathan \u0026amp; Leahy, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). The validity of approximating HRV by the use of smartphone PPG (SPPG) has been suggested in small-scale studies with healthy individuals (Holmes et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Peng et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Vondrasek et al., \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eAs previously stated, the topic of HRV in psychiatry is intensely researched, and today’s universal use of smartphones potentially offers a simple and cost-effective way of estimating a depressed patient’s HRV. Although suggested for psychophysiological research (Heathers, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Liu et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), to this date, the promising idea of approximating a depressed patient’s HRV via SPPG alone has not yet been implemented. Thus, we developed \u003cem\u003ePULSAR\u003c/em\u003e, an SPPG-based HRV application, and validated its performance in depressed patients and healthy individuals through simultaneous ECG-based HRV assessment.\u003c/p\u003e\u003cp\u003eIn this study, it was hypothesized that\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003col\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eHRV parameters derived from SPPG correlate with those derived from ECG (analytical validation).\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eSPPG-derived HRV parameters differ significantly between depressed patients and healthy individuals (clinical validation).\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003c/ol\u003e"},{"header":"Methods","content":"\u003cp\u003eDesign and participants\u003c/p\u003e\u003cp\u003eWe conducted a comparative, observational, cross-sectional validity study with 15 depressed patients and 15 healthy individuals. Depressed subjects (mean age 35.5 years, SD = 12.8 years, 9 males, 6 females) were recruited from the hospita’s specialized wards for depressive disorders. Inclusion criteria were age 18–65 years and a moderate to severe depressive episode following the criteria of ICD-10 as confirmed by a senior psychiatrist, with severity levels assessed by the Montgomery–Åsberg Depression Rating Scale (MADRS) (Montgomery \u0026amp; Asberg, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e1979\u003c/span\u003e). MADRS is a ten-item questionnaire on depressive symptoms, assessed by a mental health professional. It yields a score from 0 to 60 and allows the classification of a depressive episode from mild to severe and has been described as the gold standard clinician rating scale for depression (Jauhar \u0026amp; Morrison, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Exclusion criteria were any additional currently untreated major psychiatric conditions, any current non-psychiatric conditions, any forms of chronic cardiac arrhythmia or treatments interfering with ANS regulation of heartbeat. Healthy individuals (mean age = 35.7 years, SD = 13.6 years, 9 males, 6 females) were recruited from the private and professional network and matched precisely by age group and sex (See Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e), being subjected to the same exclusion criteria.\u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eDemographic characteristics of the study sample – categorized by age and gender\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGroup\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNumber\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eAge\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eMales\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eFemales\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDepressed\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt; 27\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e27–45\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026gt; 45\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHealthy\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt; 27\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e27–45\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026gt; 45\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\u003c/div\u003e\u003cp\u003e This study was approved by the local ethics committee (Ethikkomission Zürich, BASEC ID 2023 − 01273). All participants provided written informed consent prior to participation, and their privacy rights were observed throughout the study.\u003c/p\u003e\u003cp\u003eMeasurements\u003c/p\u003e\u003cp\u003eWe conducted a short-term, 5-minute measurement of heart rate by ECG and smartphone simultaneously. Participants were resting in a 30° semi-recumbent position on an examination table and instructed to remain as immobile possible. A total of three consecutive measurements per participant was conducted to ascertain the measurement with the ideal conditions (absence of artifacts and noise in both measurement modalities).\u003c/p\u003e\n\u003ch3\u003eECG\u003c/h3\u003e\n\u003cp\u003eFor calculating ECG derived HRV, only the R peak intervals are relevant (Shaffer \u0026amp; Ginsberg, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Therefore, we used the bipolar limb recordings as sufficient measurement modalities (Meek \u0026amp; Morris, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2002\u003c/span\u003e). For ECG measurement we used Kalamed KEC-1000\u0026reg;, which recorded the electrical signal with a sampling rate of 500 Hertz (Hz). ECG curves were plotted with the corresponding Kalamed \u0026reg; software and exported in the CSV file format. We then chose the ECG channels with the most prominent R peak for HRV analysis visually.\u003c/p\u003e\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eSPPG\u003c/h2\u003e\u003cp\u003eFor recording the SPPG curve, we developed the Android \u0026reg; application \u003cem\u003ePULSAR\u003c/em\u003e. Upon launching the application, it assigns the participant a distinctive identifier and shows user instructions. The user\u0026rsquo;s index finger is placed firmly on the smartphone camera in a way that the finger fully covers the camera, and the smartphone flash illuminates the finger (see Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). During measurement, pulse peaks are detected in real-time and beats per minute are calculated and displayed. In addition, the user is given feedback on each detected pulse peak by slight vibration of the smartphone and a visual pulsating signal on the display. After the 5-minute measurement, HRV parameters are calculated (see \u003cem\u003eHRV Analysis\u003c/em\u003e below) and displayed to the user and are ready for export for further analysis.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cem\u003ePULSAR\u003c/em\u003e works by recording a video of the finger placed on the camera with a sampling rate of circa 30 Hz and adding up the amount of the red color in each video pixel (sum of red). Whenever a pulse wave passes the finger, the amount of red decreases due to the increased blood volume in some pixels, which results in a decrease of the sum of red in each video frame. Meanwhile, the smartphone flash illuminates the user\u0026rsquo;s finger and thereby accentuates the changes in red color. The SPPG pulse wave is therefore recorded by calculating the alterations of the color red over time (See Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e) in the user\u0026rsquo;s index finger. Under ideal conditions, both the systolic pulse peaks and diastolic peaks can be distinctly recorded. The delay between the ECG\u0026rsquo;s R-peak (ventricular depolarization) and the pulse peak arises from the time required for the pulse wave to propagate from the heart to the fingertip. For pulse wave detection, we used pre-existing open-source code from Erdosi Balint\u0026rsquo;s \u003cem\u003eHeartBeat\u003c/em\u003e project (Balint, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), which we adapted and expanded for the \u003cem\u003ePULSAR\u003c/em\u003e application.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eWe used the same Android \u0026reg; phone (Realme C11 \u0026reg;) for all measurements to ensure the same testing conditions for all participants.\u003c/p\u003e\u003cp\u003eHRV Analysis\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eAn overview of all Heart Rate Variability (HRV) parameters (Marek, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e1996\u003c/span\u003e; Shaffer \u0026amp; Ginsberg, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) used in the current study as calculated by HeartPy\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"3\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e\u003cp\u003eTime-domain measures\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eParameter\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eUnit\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eDescription\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSDNN\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ems\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eStandard deviation of all NN intervals\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSDSD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ems\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eStandard deviation of differences between adjacent NN intervals.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRMSSD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ems\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eRoot mean square of successive RR interval differences\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePNN20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePercentage of successive RR intervals that differ by more than 20\u0026thinsp;ms\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePNN50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePercentage of successive RR intervals that differ by more than 50\u0026thinsp;ms\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eNon-linear measures\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSD1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ems\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePoincar\u0026eacute; plot standard deviation perpendicular the line of identity\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSD2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ems\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePoincar\u0026eacute; plot standard deviation along the line of identity\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSD1/SD2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eRatio of SD1-to-SD2\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eFrequency-domain measures\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVLF\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ems\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eAbsolute power of the very-low-frequency band (0.0033\u0026ndash;0.04\u0026thinsp;Hz)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLF\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ems\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eAbsolute power of the low-frequency band (0.04\u0026ndash;0.15\u0026thinsp;Hz)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHF\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ems\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eAbsolute power of the high-frequency band (0.15\u0026ndash;0.4\u0026thinsp;Hz)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eP_TOTAL\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ems\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eVariance of all NN intervals (0\u0026ndash;0.4 Hz)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLF_PERC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eRelative power of the low-frequency band (0.04\u0026ndash;0.15\u0026thinsp;Hz)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHF_PERC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eRelative power of the high-frequency band (0.15\u0026ndash;0.4\u0026thinsp;Hz)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLF_NU\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003enu\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eLF power in normalized units: LF / (P_TOTAL - VLF) * 100\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHF_NU\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003enu\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eHF power in normalized units: HF / (P_TOTAL - VLF) * 100\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eHRV analysis was conducted using \u003cem\u003eHeartPy\u003c/em\u003e (van Gent, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), an open-source \u003cem\u003ePython\u003c/em\u003e library, previously validated for ECG and PPG assessment of HRV (van Gent et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; van Gent et al., \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Vićentić et al., \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Initially, ECG curves were cropped to match the exact measurement timespan of the 5-minute SPPG measurement. ECG and SPPG curves were analyzed using the \u003cem\u003eprocess\u003c/em\u003e function of \u003cem\u003eHeartPy\u003c/em\u003e, using Welch\u0026rsquo;s method (Welch, \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e1967\u003c/span\u003e) to extract the frequency spectrum for the frequency-domain. In some cases, SPPG curves showed some baseline wandering (Zhao et al., \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), which posed a challenge to the calculation of peak-peak intervals. We applied \u003cem\u003eHeartPy\u003c/em\u003e\u0026rsquo;s filtering function \u003cem\u003eremove_baseline_wander\u003c/em\u003e to correct the affected curves. In rarer instances, SPPG pulse peak amplitudes were not distinctive enough to obtain an accurate peak-peak interval. To solve this, the \u003cem\u003eenhance_peaks\u003c/em\u003e function of \u003cem\u003eHeartPy was applied\u003c/em\u003e.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003eStatistical analysis\u003c/h2\u003e\u003cp\u003eFor comparison between ECG measurements and Smartphone-based PULSAR analysis: Descriptive statistics were calculated to summarize the distributional aspects of both ECG and smartphone data. For each assessed HRV variable, the mean, median, standard deviation, and interquartile range were computed. A paired t-test was conducted for each variable to identify statistically significant differences between ECG and smartphone measurements. P-values were reported to assess the significance of these differences. Pearson correlation coefficients were used to quantify the linear association between the two methods and results were visualized in a heatmap to provide an overview of potential discrepancies. To visually inspect the relationship between measurements from both methods, scatterplots were created for each variable.\u003c/p\u003e\u003cp\u003eTo evaluate systematic deviations and biases between the two measurement methods, a Bland-Altman analysis was performed. This method plotted the mean of the two measurements against their difference for each variable, providing visual insight into agreement, systematic deviations, and outliers. Additionally, a statistical bias analysis was conducted to compare mean biases between healthy and depressed groups using t-tests to determine whether observed biases differed significantly.\u003c/p\u003e\u003cp\u003eComparison between patients with depression and healthy controls:\u003c/p\u003e\u003cp\u003eTo assess the validity of the smartphone method in distinguishing between healthy and depressed individuals and its agreement with ECG data, a comprehensive analysis was conducted. Descriptive statistics and mean comparisons were performed for each variable between healthy and depressed individuals, separately for each method (ECG and smartphone) to determine whether differences between the groups were statistically significant. To account for multiple comparisons, the Benjamin-Hochberg correction (FDR-BH) was applied, balancing Type-I and Type-II errors while maintaining statistical power. Given the high correlations between HRV parameters, a stricter Bonferroni correction would have been too conservative, potentially masking true group differences. FDH-BH retained the highest number of significant results, reinforcing the robustness of observed differences without inflating false positives. Z-scores were calculated for ECG and smartphone variables for standardized comparisons, and the results were illustrated using box-and-whisker plots for healthy and depressed individuals, providing a visual representation of group differences.\u003c/p\u003e\u003cp\u003eFinally, to evaluate the predictive accuracy of both methods in distinguishing between depressed and healthy individuals, receiver operating characteristic (ROC) analyses were performed. AUC values, sensitivity, and specificity were calculated for each HRV variable.\u003c/p\u003e\u003cp\u003eIn our statistical analysis we omitted the VLF parameter since its measurement is discouraged in short-term (e.g. \u0026le; 5min) analysis of HRV. Additionally, the HF_NU parameter was excluded as it is mathematically complementary to the LF_NU parameter (Marek, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e1996\u003c/span\u003e), resulting in identical correlations and providing no additional analytical value.\u003c/p\u003e\u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eComparison of smartphone and ECG results\u003c/p\u003e\u003cp\u003eSmartphone derived HRV values yielded higher mean values for most HRV parameters, exhibiting significant differences (see descriptive results in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). However, moderate to high correlations were found for most variables (e.g. BPM, P_TOTAL, LF), indicating a strong methodological agreement between the two measurement methods (see heatmap in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e for further details). In contrast, variables with lower associations, such as SD1/SD2 or LF_PERC, indicate particular inconsistencies of results for the different methods of assessment.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eDescriptive statistics of and associations between the ECG- and smartphone-derived HRV parameters\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"9\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eECG Mean\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eECG SD\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eECG Median (IQR)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eSmartphone Mean\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eSmartphone SD\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eSmartphone Median (IQR)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003et-test p-value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e\u003cp\u003eCorrelation\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBPM\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e74.94\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e13.44\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e69.87 (64.20; 84.53)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e75.25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e13.12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e70.06 (64.90; 84.40)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.095\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.997\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSDNN\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e46.75\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e21.88\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e41.57 (30.13; 67.06)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e66.28\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e22.43\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e60.28 (47.90; 82.05)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.764\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSDSD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e23.85\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e17.58\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e19.31 (10.55; 28.41)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e47.08\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e13.84\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e43.71 (34.58; 54.69)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.528\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRMSSD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e35.38\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e23.70\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e31.55 (16.36; 36.57)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e67.42\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e18.38\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e64.45 (51.84; 80.86)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.695\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePNN20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.39\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.41 (0.17; 0.55)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.75\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.75 (0.69; 0.79)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.791\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePNN50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.07 (0.01; 0.12)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.37\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.35 (0.27; 0.44)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.757\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSD1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e25.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e16.75\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e22.31 (11.57; 25.85)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e47.47\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e12.77\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e45.56 (36.66; 57.14)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.682\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSD2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e59.96\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e27.29\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e55.12 (41.08; 82.35)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e69.40\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e29.52\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e63.64 (49.81; 85.39)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.006\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.817\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSD1/SD2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.41\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.36 (0.29; 0.51)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.76\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.74 (0.60; 1.01)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.123\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLF\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e852.88\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e783.81\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e465.33 (186.68; 1608.14)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e935.12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e727.58\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e736.28 (330.93; 1432.01)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.302\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.842\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHF\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e601.92\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e797.59\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e279.64 (111.63; 813.59)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1243.06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e992.14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1014.77 (542.54; 1539.74)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.893\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eP_TOTAL\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1454.81\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1469.87\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e793.00 (335.90; 2388.71)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e2178.18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1587.28\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1873.30 (1119.03; 2938.36)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.908\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLF_PERC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e37.63\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e11.94\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e39.09 (27.44; 47.15)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e32.80\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e11.32\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e33.02 (24.05; 40.36)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.072\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.261\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHF_PERC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e23.19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e12.76\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e21.25 (13.41; 30.83)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e46.23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e14.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e47.25 (34.92; 56.82)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.309\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLF_NU\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e62.76\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e15.80\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e66.48 (56.59; 73.80)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e41.97\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e14.22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e41.69 (28.10; 52.84)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.314\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eAccordingly, scatterplots in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e demonstrate a clear alignment along the trendlines, indicating a strong overall agreement between the two methods. For variables such as BPM, LF, or P_TOTAL, data points closely follow the line of best fit, suggesting consistent measurement performance. Systematic deviations are observed in variables such as SDNN, SDSD, PNN20, or SD1, where smartphone measurements tend to be slightly higher at lower ECG values but converge and align at higher levels. In contrast, variables such as SD1/SD2 and LF_PERC exhibit a greater degree of scatter, indicating potential measurement variability while still suggesting an underlying agreement trend.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eSimilarly, the Bland-Altman analyses in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e demonstrate an overall strong agreement between the two methods for most variables. Variables such as BPM, SDNN, and SD2 show differences that are consistently centered close to the zero line, with only a few outliers outside the 95% limits of agreement.\u003c/p\u003e\u003cp\u003eIn contrast, systematic deviations are observed for variables such as PNN20, SD1, RMSSD, and SDSD, where smartphone values tend to be higher at lower mean levels but align more closely with ECG measurements as the means increase. These patterns suggest a systematic (and thus predictable) shift, which further supports the trends identified in the scatterplots.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eThe bias analysis confirms a consistent level of agreement between the ECG and smartphone measurements across healthy and depressed groups for most variables (see Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). A clear exception is SD1/SD2, where a significant group-specific difference in bias suggests a systematic deviation. Additionally, SDSD and PNN20 variables are close to significance, suggesting trends toward group-specific bias that warrant further investigation.\u003c/p\u003e\u003cp\u003eFor most variables, including BPM, SDNN, LF, HF, and P_TOTAL, the mean biases remain comparable across groups, underscoring the robustness of both measurement methods. These findings complement the Bland-Altman results, supporting the reliability of the smartphone as a viable alternative to ECG for most HRV parameters.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eBias analysis comparing ECG- and smartphone-derived HRV parameters in healthy and depressed individuals.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHealthy Mean Bias\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eHealthy SD\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eDepressed Mean Bias\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eDepressed SD\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003et-statistic\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003ep-value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBPM\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.59\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.27\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-0.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.58\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e-1.51\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.143\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSDNN\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-17.42\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e17.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-21.64\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e13.39\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.75\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.457\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSDSD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-17.71\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e13.39\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-28.76\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e16.14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e2.04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.051\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRMSSD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-26.78\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e13.06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-37.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e19.47\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.74\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.093\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePNN20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.29\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-0.42\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.88\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.07\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePNN50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-0.25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.924\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSD1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-18.57\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e9.62\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-26.34\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e13.77\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.79\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.084\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSD2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-8.52\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e22.65\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-10.35\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e10.33\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.28\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.778\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSD1/SD2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-0.47\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.27\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e2.77\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.01\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLF\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-31.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e568.95\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-133.45\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e226.58\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.65\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.522\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHF\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-691.12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e358.25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-591.16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e542.71\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e-0.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.556\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eP_TOTAL\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-722.13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e651.14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-724.61\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e704.62\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.992\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLF_PERC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.91\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e13.58\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e8.75\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e14.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e-1.55\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.131\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHF_PERC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-19.64\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e8.98\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-26.45\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e20.28\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.244\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLF_NU\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e17.26\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e11.54\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e24.34\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e-1.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.279\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eComparison of healthy and depressed population\u003c/p\u003e\u003cp\u003eThe results of the group comparisons indicate that ECG-based HRV variables effectively differentiate between depressed and healthy individuals, with smartphone measurements largely replicating these findings (See Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e, Figs.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ea \u0026amp; \u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eb). To control for multiple comparisons, p-values were adjusted using the FDR-BH correction, ensuring a balanced approach between Type-I and Type-II errors. BPM and LF show strong agreement between methods, with similar means and consistent group differences, indicating their reliability in detecting depressive states. Systematic shifts were observed for SDNN, SD2, and P_TOTAL, where smartphone measurements tended to be higher than ECG values, yet group differences remained comparable between the two assessment methods. These differences remained statistically significant after correction, supporting the robustness of these HRV markers. In contrast, SD1, RMSSD, HF, and PNN20 did not remain significant after correction, indicating limited discriminatory power.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eDescriptive statistics of and comparison of HRV parameters between healthy and depressed individuals for both ECG- and smartphone-derived measurements\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"11\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"5\" nameend=\"c6\" namest=\"c2\"\u003e\u003cp\u003eECG\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"5\" nameend=\"c11\" namest=\"c7\"\u003e\u003cp\u003eSmartphone\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMean Healthy\u003c/p\u003e\u003cp\u003e\u0026plusmn; SD\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eMean Depressed\u003c/p\u003e\u003cp\u003e\u0026plusmn; SD\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eDifference\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003ep-value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eAdj. p-value (FDR-BH)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eMean Healthy\u003c/p\u003e\u003cp\u003e\u0026plusmn; SD\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003eMean Depressed\u003c/p\u003e\u003cp\u003e\u0026plusmn; SD\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e\u003cp\u003eDifference\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c10\"\u003e\u003cp\u003ep-value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c11\"\u003e\u003cp\u003eAdj. p-value (FDR-BH)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBPM\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e68.23\u0026thinsp;\u0026plusmn;\u0026thinsp;8.20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e81.65\u0026thinsp;\u0026plusmn;\u0026thinsp;14.51\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-13.42\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.004\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e0.008\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c7\"\u003e\u003cp\u003e68.82\u0026thinsp;\u0026plusmn;\u0026thinsp;8.09\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c8\"\u003e\u003cp\u003e81.69\u0026thinsp;\u0026plusmn;\u0026thinsp;14.21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e-12.88\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e0.005\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e\u003cb\u003e0.021\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSDNN\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e59.52\u0026thinsp;\u0026plusmn;\u0026thinsp;21.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e33.98\u0026thinsp;\u0026plusmn;\u0026thinsp;14.19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e25.54\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e0.008\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c7\"\u003e\u003cp\u003e76.94\u0026thinsp;\u0026plusmn;\u0026thinsp;23.40\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c8\"\u003e\u003cp\u003e55.62\u0026thinsp;\u0026plusmn;\u0026thinsp;15.86\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e21.32\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e0.007\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e\u003cb\u003e0.021\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSDSD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e33.28\u0026thinsp;\u0026plusmn;\u0026thinsp;20.06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e14.43\u0026thinsp;\u0026plusmn;\u0026thinsp;6.88\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e18.85\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.002\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e0.008\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c7\"\u003e\u003cp\u003e50.99\u0026thinsp;\u0026plusmn;\u0026thinsp;13.66\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c8\"\u003e\u003cp\u003e43.18\u0026thinsp;\u0026plusmn;\u0026thinsp;13.32\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e7.80\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e0.125\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e0.144\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRMSSD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e47.34\u0026thinsp;\u0026plusmn;\u0026thinsp;26.06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e23.42\u0026thinsp;\u0026plusmn;\u0026thinsp;13.32\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e23.92\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.004\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e0.008\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c7\"\u003e\u003cp\u003e74.12\u0026thinsp;\u0026plusmn;\u0026thinsp;19.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c8\"\u003e\u003cp\u003e60.72\u0026thinsp;\u0026plusmn;\u0026thinsp;15.55\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e13.40\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e0.044\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e0.072\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePNN20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e0.49\u0026thinsp;\u0026plusmn;\u0026thinsp;0.21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e0.30\u0026thinsp;\u0026plusmn;\u0026thinsp;0.25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.037\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.056\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c7\"\u003e\u003cp\u003e0.77\u0026thinsp;\u0026plusmn;\u0026thinsp;0.06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c8\"\u003e\u003cp\u003e0.72\u0026thinsp;\u0026plusmn;\u0026thinsp;0.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e0.017\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e\u003cb\u003e0.036\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePNN50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e0.18\u0026thinsp;\u0026plusmn;\u0026thinsp;0.18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e0.07\u0026thinsp;\u0026plusmn;\u0026thinsp;0.11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.053\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.072\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c7\"\u003e\u003cp\u003e0.42\u0026thinsp;\u0026plusmn;\u0026thinsp;0.11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c8\"\u003e\u003cp\u003e0.32\u0026thinsp;\u0026plusmn;\u0026thinsp;0.09\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e0.007\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e\u003cb\u003e0.021\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSD1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e33.47\u0026thinsp;\u0026plusmn;\u0026thinsp;18.43\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e16.56\u0026thinsp;\u0026plusmn;\u0026thinsp;9.42\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e16.91\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.004\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e0.008\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c7\"\u003e\u003cp\u003e52.04\u0026thinsp;\u0026plusmn;\u0026thinsp;13.14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c8\"\u003e\u003cp\u003e42.89\u0026thinsp;\u0026plusmn;\u0026thinsp;10.98\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e9.15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e0.048\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e0.072\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSD2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e75.35\u0026thinsp;\u0026plusmn;\u0026thinsp;26.18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e44.57\u0026thinsp;\u0026plusmn;\u0026thinsp;18.70\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e30.78\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e0.008\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c7\"\u003e\u003cp\u003e83.87\u0026thinsp;\u0026plusmn;\u0026thinsp;31.42\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c8\"\u003e\u003cp\u003e54.92\u0026thinsp;\u0026plusmn;\u0026thinsp;19.22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e28.95\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e0.005\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e\u003cb\u003e0.021\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSD1/SD2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e0.45\u0026thinsp;\u0026plusmn;\u0026thinsp;0.20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e0.37\u0026thinsp;\u0026plusmn;\u0026thinsp;0.11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.08\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.172\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.215\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c7\"\u003e\u003cp\u003e0.68\u0026thinsp;\u0026plusmn;\u0026thinsp;0.20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c8\"\u003e\u003cp\u003e0.84\u0026thinsp;\u0026plusmn;\u0026thinsp;0.25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e-0.16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e0.056\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e0.076\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLF\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e1249.94\u0026thinsp;\u0026plusmn;\u0026thinsp;788.39\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e455.83\u0026thinsp;\u0026plusmn;\u0026thinsp;559.66\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e794.10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.004\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e0.008\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c7\"\u003e\u003cp\u003e1280.95\u0026thinsp;\u0026plusmn;\u0026thinsp;727.36\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c8\"\u003e\u003cp\u003e589.28\u0026thinsp;\u0026plusmn;\u0026thinsp;557.85\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e691.67\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e0.007\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e\u003cb\u003e0.021\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHF\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e934.90\u0026thinsp;\u0026plusmn;\u0026thinsp;993.73\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e268.94\u0026thinsp;\u0026plusmn;\u0026thinsp;304.39\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e665.96\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.019\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e0.032\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c7\"\u003e\u003cp\u003e1626.02\u0026thinsp;\u0026plusmn;\u0026thinsp;1165.27\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c8\"\u003e\u003cp\u003e860.11\u0026thinsp;\u0026plusmn;\u0026thinsp;605.71\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e765.91\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e0.032\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e0.060\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eP_TOTAL\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e2184.84\u0026thinsp;\u0026plusmn;\u0026thinsp;1636.92\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e724.78\u0026thinsp;\u0026plusmn;\u0026thinsp;808.59\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1460.06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.004\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e0.008\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c7\"\u003e\u003cp\u003e2906.97\u0026thinsp;\u0026plusmn;\u0026thinsp;1736.22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c8\"\u003e\u003cp\u003e1449.39\u0026thinsp;\u0026plusmn;\u0026thinsp;1032.60\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e1457.59\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e0.009\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e\u003cb\u003e0.023\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLF_PERC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e35.65\u0026thinsp;\u0026plusmn;\u0026thinsp;10.04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e39.61\u0026thinsp;\u0026plusmn;\u0026thinsp;13.64\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-3.97\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.372\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.429\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c7\"\u003e\u003cp\u003e34.74\u0026thinsp;\u0026plusmn;\u0026thinsp;12.43\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c8\"\u003e\u003cp\u003e30.86\u0026thinsp;\u0026plusmn;\u0026thinsp;10.15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e3.87\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e0.358\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e0.358\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHF_PERC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e22.40\u0026thinsp;\u0026plusmn;\u0026thinsp;11.55\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e23.97\u0026thinsp;\u0026plusmn;\u0026thinsp;14.23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-1.57\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.743\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.796\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c7\"\u003e\u003cp\u003e42.04\u0026thinsp;\u0026plusmn;\u0026thinsp;12.18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c8\"\u003e\u003cp\u003e50.42\u0026thinsp;\u0026plusmn;\u0026thinsp;14.94\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e-8.38\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e0.103\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e0.129\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLF_NU\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e62.39\u0026thinsp;\u0026plusmn;\u0026thinsp;14.50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e63.14\u0026thinsp;\u0026plusmn;\u0026thinsp;17.51\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-0.75\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.899\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.899\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c7\"\u003e\u003cp\u003e45.13\u0026thinsp;\u0026plusmn;\u0026thinsp;13.53\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c8\"\u003e\u003cp\u003e38.80\u0026thinsp;\u0026plusmn;\u0026thinsp;14.65\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e6.33\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e0.229\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e0.245\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eBuilding on these findings, we assessed the predictive accuracy of both methods by analyzing AUC values, sensitivity, and specificity for each variable. (See Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). Overall, AUC analysis shows high AUC values and moderate- to high sensitivity and specificity across time- and frequency domain HRV variables in both ECG and smartphone measurements, suggesting a strong discriminatory power for distinguishing between our healthy and depressed population. However, some variables, such as LF_PERC, HF_PERC and LF_NU showed inconsistent results. While the AUC values for these metrics are moderate in some cases, their overall discriminative ability (and thus clinical utility) remains limited due to minimal and statistically non-significant group differences observed earlier.\u003c/p\u003e\u003cp\u003eNotably, SD1/SD2 emerges as a variable where smartphone measurements outperform ECG by means of group differentiation, underscoring the smartphone\u0026rsquo;s potential competitive performance for selected metrics.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eArea under the curve (AUC), sensitivity and specificity values of ECG- and smartphone-derived HRV parameters for distinguishing between healthy and depressed individuals\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e\u003cp\u003eECG\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e\u003cp\u003eSmartphone\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAUC\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSensitivity\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eSpecificity\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eAUC\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eSensitivity\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eSpecificity\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBPM\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.78\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.93\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.78\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.93\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSDNN\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.84\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.73\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.73\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.8\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSDSD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.86\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.93\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.67\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.66\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.8\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRMSSD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.83\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.53\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.8\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePNN20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.71\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.53\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.93\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.76\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.73\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePNN50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.74\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.67\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.76\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.73\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSD1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.83\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.53\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.69\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.8\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSD2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.83\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.73\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.73\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSD1/SD2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.59\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.27\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.87\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLF\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.87\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.67\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.81\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.87\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.8\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHF\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.76\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.87\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.73\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.53\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.93\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eP_TOTAL\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.79\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.87\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.67\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.78\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.47\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLF_PERC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.61\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.73\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.59\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.53\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHF_PERC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.69\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.67\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.8\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLF_NU\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.55\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.67\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.64\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.33\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.93\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study presents a novel modality for approximating reduced HRV in depressed patients using only a standard smartphone without external devices, validating it against the state-of-the-art assessment of HRV via ECG. We confirmed Hypothesis 1 by demonstrating that SPPG-derived HRV parameters are highly correlated to their ECG based counterparts during simultaneous 5-minute resting measurements in patients with depression and healthy individuals. Additionally, our depressed population exhibited a significant reduction in HRV parameters across time- and frequency domain measures in both ECG and SPPG recordings compared to the healthy group, confirming Hypothesis 2, While previous studies have either extensively examined altered HRV in depression or the usage of (S)PPG as a surrogate of the gold standard ECG, to the author\u0026rsquo;s knowledge, this is the first application of a smartphone-only approach in clinically depressed patients.\u003c/p\u003e\u003cp\u003ePrevious studies have demonstrated strong correlations between ECG- and SPPG derived HRV parameters across time- and frequency domain measures (Kr\u0026oacute;lak et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Vondrasek et al., \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Zhao et al., \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Regarding analytical validation, our findings replicated these results for BPM and the time- and frequency domain parameters with one exception in each case. However, SPPG measurement tended to overestimate most HRV variables, particularly at lower values, leading to significant differences between both measurement methods. An exception to this trend was the LF power parameter, which exhibited both a high correlation and a non-significant difference between SPPG and ECG. The robustness of this parameter was confirmed by additional analysis (Bland-Altman and bias analysis). We partly attribute this systematic overestimation to motion artifacts during our SPPG measurements. Involuntary finger movement during (S)PPG measurement can introduce gaps and falsely detected pulse peaks, significantly altering HRV results (Kr\u0026oacute;lak et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Additionally, we link the overestimation to \u003cem\u003ePULSAR\u003c/em\u003e\u0026rsquo;s relatively low sampling rate of 30 Hz. Previous studies have shown a systematic overestimation of PPG-derived HRV parameters at lower sampling rates, with the magnitude of overestimation increasing as the sampling rate decreases (Burma et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Pelaez-Coca et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). This effect is attributed to a decrease in distinction of the pulse peak in lower sampling rates, leading to reduced precision in quantifying the interbeat intervals (Burma et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). To accurately quantify HRV parameters using \u003cem\u003ePULSAR\u003c/em\u003e, calibration algorithms to correct for overestimation need to be implemented in the application. Nevertheless, current results remain sufficiently reliable for intra- and interindividual comparison of SPPG derived HRV parameters.\u003c/p\u003e\u003cp\u003eRecent systematic reviews and meta-analyses have consistently shown significant increases in BPM and decreases in HRV parameters across time- and frequency domain measures in depressed patients (Koch et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Schiweck et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Burma et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). In terms of clinical validation, our study fully corroborates these findings through ECG-derived parameters and validates them for SPPG variables except for SDSD. This not only confirms previous research highlighting reduced HRV in depression but more importantly underscores the potential of SPPG in measuring said reduced HRV. Notably, the LF power parameter stood out again due to its strong performance: It exhibited a significant reduction in descriptive group comparisons (p\u0026thinsp;=\u0026thinsp;0.021), a narrow interquartile range with minimal overlap in the box-and-whisker plots, and robust diagnostic accuracy in the ROC analysis for detecting depression (AUC\u0026thinsp;=\u0026thinsp;0.81, sensitivity\u0026thinsp;=\u0026thinsp;0.87, specificity\u0026thinsp;=\u0026thinsp;0.80). The reported literature overlooks the non-linear parameters (SD1, SD2) as well as LF_PERC, HF_PERC and LF_NU of the frequency domain, suggesting a secondary importance of those parameters for stratifying depression.\u003c/p\u003e\u003cp\u003eThese findings prompted a closer examination of the frequency domain of HRV. ANS influences lead to a fluctuating heart rate signal over time. Through spectral analysis, these oscillatory components can be broken down into different frequency bands (Malliani et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e1991\u003c/span\u003e), with the LF band being set at 0.03\u0026ndash;0.15 Hz and the HF band at 0.15\u0026ndash;0.4 Hz (Eckberg, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e1997\u003c/span\u003e). The LF band is proposed to be influenced by both PNS and SNS activity, whereas SNS activation appears to play a dominant role in its increase. The HF band is considered to be solely influenced by PNS activity (Sgoifo et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). In our depressed population, both LF and HF power parameters showed significant decreases, which suggests reduced activity of SNS as well as PNS branches of the ANS in depressed patients. Given LF power's robustness as the most reliable variable measured by the \u003cem\u003ePULSAR\u003c/em\u003e application, SPPG-derived measurements of this parameter can be proposed as a potential biomarker for gaining meaningful insights into the PNS and SNS activity of depressed individuals.\u003c/p\u003e\u003cp\u003eHRV has been suggested as a viable diagnostic biomarker for mental disorders in general (Beauchaine \u0026amp; Thayer, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Tomasi et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Villar de Araujo et al., 2023) and depression in particular (Gullett et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Mulcahy et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Schiweck et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Patients with recent suicide attempts have exhibited significant reduction in HRV parameters across time- and frequency domain measures (R\u0026uuml;esch et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Beyond its diagnostic utility, heart rate analysis has demonstrated predictive potential for antidepressant medication with serotonin-norepinephrine reuptake inhibitors (SNRIs) (Olbrich et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Furthermore, a recent study utilized HRV parameters to discriminate between depressive responders and non-responders to ketamine therapy, underscoring its potential as a predictive biomarker for treatment outcomes (Meyer et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Given that SPPG measurement of HRV offers a non-invasive, easy-access and cost-effective modality (De Ridder et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), its potential in detecting, stratifying and analysis of the predictiveness of HRV-derived biomarkers merits significant consideration.\u003c/p\u003e\u003cp\u003eStrengths and Limitations\u003c/p\u003e\u003cp\u003eWe investigated a total of 14 common HRV parameters in addition to beats per minute (BPM) and conducted rigorous descriptive and visual analyses. This allowed us to perform a broad analytical and clinical validation process and ascertain the specific SPPG derived HRV parameters with the highest (e.g. LF power) and the least (e.g. SDSD) potential.\u003c/p\u003e\u003cp\u003eFor this study, we used a relatively small dataset of n\u0026thinsp;=\u0026thinsp;30 participants. While this sample size was deemed appropriate for an initial feasibility study in this research area, future studies should include a larger cohort to strengthen our findings.\u003c/p\u003e\u003cp\u003eIdentical measurement conditions (same smartphone, reclining position, finger placement, room lighting) were maintained for all participants. In practical applications, however, \u003cem\u003ePULSAR\u003c/em\u003e would be used in a variety of settings where external factors, particularly lighting conditions, could affect measurement precision.\u003c/p\u003e\u003cp\u003eAdditionally, \u003cem\u003ePULSAR\u0026rsquo;s\u003c/em\u003e SPPG measurements seem to be susceptible to movement artifacts, which can distort the recorded pulse wave and the derived HRV parameters. To mitigate this, we selected the result least affected by artifacts from three measurements and applied \u003cem\u003eHeartPy\u0026rsquo;s\u003c/em\u003e filtering functions. For future iterations, we aim to implement detection and correction algorithms into \u003cem\u003ePULSAR\u003c/em\u003e to automatically reduce movement artifacts.\u003c/p\u003e\u003cp\u003eFuture research should focus on longitudinally assessing HRV parameters in depressed patients using \u003cem\u003ePULSAR\u003c/em\u003e, with particular emphasis on the LF power parameter. Such studies would not only reinforce our findings but also provide patients with meaningful insights into their ANS by offering a user-friendly, HRV-derived score. Numerous studies have demonstrated the efficacy of HRV biofeedback training (Goessl et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Laborde et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Future iterations of our smartphone application could integrate biofeedback techniques, such as breathing exercises (Steffen et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), adding a therapeutic dimension to \u003cem\u003ePULSAR\u003c/em\u003e.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn terms of analytical validation, our study demonstrated strong to very strong correlations between most SPPG-derived HRV parameters and their ECG counterparts. However, SPPG measurements exhibited systematic overestimations, particularly at lower HRV values. We attribute this to motion artifacts and reduced sampling rate. Regarding clinical validation, our depressed population revealed a significant reduction in HRV measured by SPPG- and confirmed by ECG-based measurements. Among the assessed variables, the frequency-domain LF power parameter (r\u0026thinsp;=\u0026thinsp;0.84, p\u0026thinsp;=\u0026thinsp;0.021, AUC\u0026thinsp;=\u0026thinsp;0.81, sensitivity\u0026thinsp;=\u0026thinsp;0.87, specificity\u0026thinsp;=\u0026thinsp;0.80), which is chiefly dependent on sympathetic nervous system activity, emerged as the most reliable metric, demonstrating strong potential for detecting HRV reductions in depressed patients. These findings support the theory of autonomic dysregulation with altered sympathetic and reduced parasympathetic nervous system activity in depression.\u003c/p\u003e\u003cp\u003eIn conclusion, our study demonstrated the feasibility of approximating HRV reduction in depression using SPPG alone, without the need for external devices. This further reinforces the role of HRV as a biomarker in depression and highlights smartphone-based measurement as an accessible and cost-effective modality for its acquisition. Future studies should aim to address the systematic overestimations observed in SPPG measurements, conduct longitudinal assessment of HRV in larger groups of depressed patients and incorporate biofeedback techniques into our smartphone application.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eFunding\u003c/p\u003e\n\u003cp\u003eThis research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors. Open access funding for this publication was provided by the University of Zurich.\u003c/p\u003e\n\u003cp\u003eCompeting interests\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\n\u003cp\u003eData Availability\u003c/p\u003e\n\u003cp\u003eData will be made available upon reasonable request to the corresponding author.\u003c/p\u003e\n\u003cp\u003eEthical Approval\u003c/p\u003e\n\u003cp\u003eThis study was approved by the local ethics committee (Ethikkomission Z\u0026uuml;rich, BASEC ID 2023-01273) on November 23, 2023.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eInformed Consent\u003c/p\u003e\n\u003cp\u003eAll participants provided written informed consent prior to participation, and their privacy rights were observed throughout the study.\u003cbr\u003e \u003c/p\u003e\u003cp\u003eCRediT authorship contribution statement\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eLion D. Comfort\u003c/strong\u003e: Writing \u0026ndash; original draft, investigation, software, conceptualization, data curation. \u003cstrong\u003eMario M\u0026uuml;ller\u003c/strong\u003e: Writing \u0026ndash; original draft, formal analysis, visualization, validation, data curation. \u003cstrong\u003eErich Seifritz\u003c/strong\u003e: Writing \u0026ndash; review and editing, supervision. \u003cstrong\u003eSebastian Olbrich:\u0026nbsp;\u003c/strong\u003eWriting \u0026ndash; review and editing, project administration, supervision, conceptualization, validation\u003c/p\u003e\n"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAllen, J. (2007). Photoplethysmography and its application in clinical physiological measurement. \u003cem\u003ePhysiol Meas\u003c/em\u003e,\u003cem\u003e 28\u003c/em\u003e(3), R1-39. https://doi.org/10.1088/0967-3334/28/3/R01\u003c/li\u003e\n\u003cli\u003eAlvares, G. A., Quintana, D. S., Hickie, I. B., \u0026amp; Guastella, A. J. (2016). 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An error-bounded median filter for correcting ECG baseline wander. \u003cem\u003eHealth Inf Sci Syst\u003c/em\u003e,\u003cem\u003e 11\u003c/em\u003e(1), 45. https://doi.org/10.1007/s13755-023-00235-w\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"HRV, SPPG, smartphone, depression, biomarker, ECG","lastPublishedDoi":"10.21203/rs.3.rs-7004304/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7004304/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eHeart rate variability (HRV) describes time fluctuations between consecutive heart beats, providing insight into the sympathetic and parasympathetic branches of the autonomic nervous system. In depressed patients, HRV has shown reduction due to autonomic dysregulation. Traditionally, its measurement is conducted using electrocardiography (ECG). A novel approach is measurement through a conventional smartphone via photoplethysmography (SPPG), which has not yet been explored in depressed patients. Thus, we developed \u003cem\u003ePULSAR\u003c/em\u003e, an SPPG application which measures the user\u0026rsquo;s pulse wave using the smartphone camera and flash, without the need for external devices, to calculate HRV. To validate \u003cem\u003ePULSAR\u003c/em\u003e, simultaneous 5-minute resting-state ECG and SPPG measurements were conducted in 15 healthy individuals and 15 depressed patients. In terms of analytical validation, the SPPG-derived HRV parameters demonstrated high correlations to their ECG- derived counterparts while systematically overestimating most variables. This overestimation is attributed to motion artifacts and the low sampling rate in SPPG measurements. Regarding clinical validation, HRV parameters in the depressed population were significantly reduced compared to healthy controls in both ECG and SPPG recordings. The frequency-domain parameter LF power, which primarily reflects sympathetic nervous system activity, emerged as the most robust variable for both clinical and analytical validation. Its SPPG-derived value showed a strong correlation with its ECG counterpart (r\u0026thinsp;=\u0026thinsp;0.84) and a significant reduction in depressed patients (p\u0026thinsp;=\u0026thinsp;0.021). Future iterations of the \u003cem\u003ePULSAR\u003c/em\u003e application should focus on addressing its overestimation of HRV parameters, implementing longitudinal measurements in depressed patients, and potentially incorporating biofeedback techniques to introduce a therapeutic dimension.\u003c/p\u003e","manuscriptTitle":"Validation of Smartphone-Based Heart Rate Variability Measurement Against ECG in Patients with Depression","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-07-22 16:16:21","doi":"10.21203/rs.3.rs-7004304/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"458fff1f-4c45-40fb-871d-b3f860f7a27c","owner":[],"postedDate":"July 22nd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-11-15T08:38:58+00:00","versionOfRecord":[],"versionCreatedAt":"2025-07-22 16:16:21","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7004304","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7004304","identity":"rs-7004304","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}