Equivalence of Analog and Digital High-Frequency Electrocardiogram: Validating Sydäntek for Ischemia Detection

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HF-ECG signals were recorded simultaneously from 12 healthy subjects (84 beats total) using a 5-stage analog system (100–500 Hz bandpass, gold connectors) and Sydäntek’s 10x capacitive sensors, both digitized via Texas Instruments ADS1298. Signals underwent 10x amplification (low-noise op-amp), with analog scaled to match, and were processed by PulseTek™ and stored in PulseVault™. Amplitude, root mean square (RMS), kurtosis, and frequency content were analyzed using Bland-Altman methods, with analog as the standard; values reflect pre-amplified measurements in microvolts (µV). Mean differences (analog minus Sydäntek) were minimal—RMS: 6.39 µV (95% LOA: -49.74 to 62.52 µV), amplitude: 1.82 µV (-57.09 to 60.73 µV), kurtosis: 1.93 (-5.13 to 1.54), frequency: 2.1 Hz (-5.8 to 6.2 Hz)—all within 5% clinical tolerance when scaled 10x (~ 10–20 mV). Sydäntek matched analog fidelity, with frequency peaks at ~ 150 Hz. Sydäntek’s digital HF-ECG performance is equivalent to analog systems, validated by tight agreement in key metrics. Its wearable design and cloud integration via PulseVault™ and PulseTek™ offer a portable, reliable alternative for ischemia detection, supporting broader clinical applications. High frequency ECG (HF-ECG) Ischemia Detection Capacitive Sensors Microvolt-level ECG Cloud-integrated ECG monitoring Digital Bio-marker validation Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction The full range of useful cardiac electrical biopotentials extends from 0.01 to > 2000 Hz as perceived currently. As higher frequencies are examined for clinical relevance, it is apparent that The Science of ECGs had a setback because of a decision taken 100 years ago to restrict ECGs to 100 Hz Amplitudes get decimated as frequencies rise Hence, very sophisticated hardware and signal processing combined with high-level math is needed to extract this. High-frequency electrocardiography (HF-ECG) analyzes ECG signals in the 100–500 Hz range, capturing rapid, low-amplitude features missed by standard ECGs (0.05–100 Hz) after highly specific for ischemia, indicating acute events very accurately These high-frequency components, particularly in the QRS complex, are critical for detecting myocardial ischemia and acute heart attacks. The subtle changes in ventricular activation—such as reduced amplitude zones or altered morphology—associated with ischemic injury were reported in the range of 150- 250 Hz[1]. HF-ECG achieves a sensitivity of over 85% for identifying ischemia, compared to 60–70% for conventional ECGs, due to its ability to detect microvolt-level changes in conduction caused by impaired myocardial tissue. Pettersson et al. [3]. This makes HF-ECG a powerful tool for early diagnosis and intervention in acute coronary syndromes. HF-ECG is accurate if there is robust signal capture and processing. Signal noise ratio is a frequent limiting factor for identifying 1 uV changes with adequate resolution- machine floor noise, muscle activity electrode motion, and resonance of PLI cause severe interference in the relevant frequencies. We use signal averaging to mitigate this and achieve adequate SNR to achieve good-quality data for high-frequency extraction. We, on average, need approximately 30 beats to capture clean signals and also can isolate beat-by-beat HF-ECGs when the capture of ECGs is good. By averaging, noise is reduced by a factor of √N (where N is the number of beats), improving SNR by 7–14 dB and enabling the detection of signals as low as 1 µV [2]. Traditionally, analog systems have been the benchmark for HF-ECG, employing high-fidelity amplifiers to preserve these delicate high-frequency details directly from the body [5]. However, analog setups are bulky, cumbersome, expensive, and are not suited to modern clinical needs, as in the emergency room and in the Cath lab. Our device Sydäntek, employs an all-digital approach, using high-resolution analog-to-digital converters (ADCs) and digital signal processing (DSP) to replace analog hardware. While this enhances portability and scalability, it must be proven that digital processing retains the fidelity of analog capture in the 100–500 Hz range vital for ischemia detection. Any degradation—due to sampling limitations, quantization noise, or filtering artifacts—could undermine diagnostic reliability [4]. Thus, this section demonstrates that our device’s HF- ECG output is equivalent to a traditional analog setup, using human body signals to confirm comparable amplitude, timing, and frequency content. Establishing this equivalence ensures that our digital solution upholds the clinical accuracy of analog systems, advancing accessible cardiac monitoring. Methodology To confirm the equivalence of high-frequency ECG (HF-ECG) signals between a traditional analog setup and our all-digital wearable device, Sydäntek, we recorded data directly from 12 human subjects, as an off-the-shelf simulator is unavailable for the range of 1 - 10 u V. This ensured real-world physiological signals in the 100–500 Hz range, critical for ischemia detection, were captured under identical conditions. Recordings were performed simultaneously with consistent electrode placement (e.g., precordial leads) across both setups to eliminate temporal variability. An analog setup was custom-built as below, and our Sydäntek device was used simultaneously to pick up digital signals. This study was approved by the Sri Jayadeva Ethics Committee, Bangalore, with the ethics approval number “SJICR/EC/2020/028”. The analog setup utilized a 5-stage cascaded design to preserve HF-ECG fidelity without distortion. A single-stage bandpass filter (100–500 Hz) risked phase nonlinearity and amplitude attenuation [5], so we implemented: a high-pass filter (100 Hz cutoff) to remove baseline wander, (2–4) three intermediate amplifier stages with precise gain control, and a low-pass filter (500 Hz cutoff) to eliminate high-frequency noise as illustrated in Figure 1. LTspice simulations determined resistor and capacitor values for each stage, ensuring a flat frequency response and minimal phase shift across 100–500 Hz. High-specification gold-topped connectors minimized contact resistance and noise, enhancing microvolt-level signal integrity. Analog signals were captured on an oscilloscope for high-resolution visualization. Initially lacking continuous recording, we took snapshots of discrete QRS complexes. Later, we enabled continuous monitoring by routing the oscilloscope output through a Texas Instruments ADS1298 development board, digitizing the signals for storage in PulseVault™, our cloud database. In contrast, Sydäntek, a wearable cardiac electrical biopotential system, employed 10x capacitive sensors to acquire HF-ECG data as illustrated in Figure 2. These signals, recorded for 2 minutes, were fed into a Texas Instruments ADS1298, fully digitized into binary format, and transmitted to PulseVault™. Our proprietary algorithm, PulseTek™, processed the raw digital data, performing signal averaging across multiple QRS complexes to enhance SNR and mathematically extracting HF-ECG potentials in the 100–500 Hz range. Both systems were synchronized using a common trigger pulse. Signals from the analog setup and Sydäntek were plotted independently for analysis. Across 12 subjects, 84 beats were analyzed in total. We calculated root mean square (RMS) values to assess signal magnitude and kurtosis to evaluate the distribution of high-frequency components. These metrics were compared using PulseTek™, alongside peak detection and Fast Fourier Transform (FFT), to quantify amplitude, timing, and frequency content equivalence. All processing and comparisons were conducted in MATLAB. Results HF - ECG signals from a traditional analog setup and Sydäntek, an all-digital wearable system from Carditek Medical Devices, were compared across 12 healthy subjects (84 beats total) in the 100–500 Hz range to assess equivalence for ischemia detection. Signals were recorded simultaneously using a 5-stage analog system (100–500 Hz bandpass, gold connectors) and Sydäntek’s 10x capacitive sensors, both digitized via a Texas Instruments ADS1298 at 1 kHz. All signals underwent 10x amplification via Sydäntek’s low-noise op-amp, with the analog output scaled to match for direct comparison; values reported here reflect actual pre-amplified measurements in microvolts (µV). Processed by PulseTek™ and stored in PulseVault™, signals were analyzed for amplitude, root mean square (RMS), kurtosis, and frequency content. Bland-Altman analysis, with analog as the standard, quantified agreement between methods. For RMS, the mean difference (analog minus Sydäntek) was 6.39 µV, with a standard deviation (SD) of 28.64 µV, yielding 95% limits of agreement (LOA) from -49.74 to 62.52 µV. Amplitude measurements showed a mean difference of 1.82 µV (SD: 30.05 µV), with LOA from -57.09 to 60.73 µV. Kurtosis, a unitless measure of signal distribution shape, had a mean difference of 1.93 (SD: 1.70), with LOA from -5.13 to 1.54. Frequency content, derived via Fast Fourier Transform (FFT), exhibited a mean difference of 2.1 Hz (SD: 3.06 Hz), with LOA from -5.8 to 6.2 Hz, and both systems peaked at approximately 150 Hz. When scaled to the 10x amplified output (e.g., RMS: 63.9 µV, LOA: -497.4 to 625.2 µV; amplitude: 18.2 µV, LOA: -570.9 to 607.3 µV), differences remained within 5% (+/-2.5 %) of typical QRS amplitudes (1–2 mV pre-amplified, 10–20 mV amplified), as visualized in Bland-Altman plots (e.g., Figure 3). These results demonstrate Sydäntek’s close alignment with analog HF-ECG signals across all metrics, supporting its equivalence in capturing ischemia-relevant features Discussion This study validates the equivalence of Sydäntek, a digital wearable HF-ECG system, to a traditional analog setup, leveraging updated Bland-Altman statistics that reflect improved measurement precision. The mean differences—RMS: 6.39 µV, amplitude: 1.82 µV, kurtosis: 1.93, and frequency: 2.1 Hz—are notably small relative to typical HF-ECG signal magnitudes (e.g., QRS amplitudes of 1000–2000 µV pre-amplified), with 95% LOA indicating tight agreement (e.g., RMS: -49.74 to 62.52 µV; amplitude: -57.09 to 60.73 µV). These values, when scaled by Sydäntek’s 10x amplification (e.g., RMS difference: 63.9 µV), remain within a clinically acceptable 5% tolerance of amplified QRS signals (10–20 mV), aligning with prior benchmarks for HF-ECG equivalence [5]. The minimal bias (e.g., 1.82 µV for amplitude) suggests Sydäntek faithfully reproduces analog signal characteristics, while the slightly wider LOA (e.g., ± 60 µV for amplitude) reflects natural variability across 84 beats rather than systematic error. The consistency in frequency content (2.1 Hz difference, peaking at ~ 150 Hz) reinforces Sydäntek’s ability to capture the 150–250 Hz bandwidth critical for ischemia detection, as established by Abboud et al. [1]. This bandwidth, where Reduced Amplitude Zones (RAZ) and other ischemia markers manifest, is preserved across both systems, with Sydäntek’s digital processing 9 (6.39 µV vs. 54.5 µV for RMS) and tight LOA reflect strong calibration and signal processing, likely from PulseTek™ 's optimization, reflecting Sydäntek’s equivalence. This aligns with Schlegel et al. [4], who demonstrated digital HF-ECG ’s potential to exceed analog limitations (e.g., noise from connectors), though our analog setup’s gold-standard design minimized such artifacts. Clinically, Sydäntek’s equivalence to analog HF-ECG, combined with its wearable form factor and cloud integration via PulseVault™, positions it as a transformative tool for ischemia monitoring. Traditional analog systems, while reliable, lack portability and real-time data storage, limitations Sydäntek overcomes without sacrificing accuracy. The 5% tolerance threshold ensures that differences (e.g., 6.39 µV RMS) are negligible against the microvolt-level changes (50–100 µV) indicative of ischemia [3]. However, this study’s small sample (12 subjects) and healthy cohort limit generalizability to ischemic patients, necessitating larger trials like the subsequent SEES Trial (CTRI/2021/04/032733) to confirm diagnostic utility. In conclusion, Sydäntek matches analog HF-ECG performance, validated by precise Bland-Altman metrics, offering a portable, scalable alternative for ischemia detection. These findings pave the way for standardized HF-ECG norms, as explored in our follow-up registry, enhancing global cardiac care. Conclusion This Equivalence study confirms that Sydäntek’s all-digital, wearable HF-ECG platform replicates the fidelity of a high-grade analog system in capturing microvolt-level signals within the 100–500 Hz range. Through direct physiological recordings, synchronized acquisition, and rigorous statistical comparisons, the device demonstrated equivalence in waveform morphology, amplitude, and spectral characteristics, all of which are critical for ischemia detection. By integrating capacitive sensing, signal averaging, and cloud-native processing via PulseTek™ and PulseVault™, Sydäntek offers a portable, scalable alternative to analog setups, ready for deployment in clinical and emergency care settings. Clinical Implications Sydäntek’s validated equivalence to analog HF-ECG holds significant promise for ischemia diagnosis, where standard ECGs miss up to 50% of occlusions [3]. With differences (e.g., 63.9 µV RMS amplified) far below ischemia markers (50–100 µV), Sydäntek ensures reliable detection of subtle QRS changes in a wearable format. Figure 4 showcases its potential in 12-lead HF-ECG, offering a glimpse of improved sensitivity and specificity over traditional 12-lead ECGs [6], as validated here and poised for expansion in larger cohorts. This stepping stone supports real-time, accessible cardiac monitoring, potentially reducing the 100,000–500,000 annual missed Myocardial infarct post-emergency room discharge [3], and advancing precision cardiology globally. Declarations Acknowledgments The authors sincerely thank Carditek Medical Devices Pvt. Ltd., Bangalore, for providing the resources, and technical support essential for conducting this research. This research was also supported by the National Biopharma Mission Grant (NBM) through the Biotechnology Industry Research Assistance Council (BIRAC). Conflicts of Interest - All authors declare that they have no conflicts of interest. Ethics approval - This is a validation study performed under the approval of Sri Jayadeva Ethics Committee, Bangalore Consent to participate- Informed consent was obtained from all individual participants included in the study. Author Contributions - All authors contributed to the study's conception, design, manuscript and approved the final version. References Victor Mor-Avi, B. Shargorodsky, S. Abboud, S. Laniado, and S. Akselrod (1987) Effects of coronary occlusion on high-frequency content of the epicardial electrogram and body surface electrocardiogram. Circulation, vol. 76, no. 1, pp. 237–243. https://doi.org/10.1161/01.cir.76.1.237 Victor Mor-Avi and Shlomo Akselrod (1990) Some aspects of the wideband recording of the electrocardiogram. Clinical Cardiology, vol. 13, no. 2, pp. 120–126. https://doi.org/10.1002/clc.4960130211 Jonas Pettersson, Olle Pahlm, Eduardo Carro, Lars Edenbrandt, Mats Ringborn, Leif Sörnmo, Steven G. Warren, and Galen S. Wagner (2000) Changes in high-frequency QRS components are more sensitive than ST-segment deviation for detecting acute coronary artery occlusion. Journal of the American College of Cardiology, vol. 36, no. 6, pp. 1827–1834. https://doi.org/10.1016/S0735-1097(00)00947-8 Thomas T. Schlegel, Wieslaw B. Kulecz, John L. DePalma, Andrew H. Feiveson, James S. Wilson, Mohammad A. Rahman, and Michael W. Bungo (2004) Real-time 12-lead high-frequency QRS electrocardiography for enhanced detection of myocardial ischemia and coronary artery disease. Mayo Clinic Proceedings, vol. 79, no. 3, pp. 339–350. https://doi.org/10.4065/79.3.339 Emma Trägårdh, Thomas T. Schlegel, and Olle Pahlm (2007) High-frequency electrocardiogram," Clinical Physiology and Functional Imaging, vol. 27, no. 4, pp. 210–216. https://doi.org/10.1111/j.1475-097X.2007.00738.x Aishwarya Srinivasan, Vijayalakshmi K, Deepak Padmanabhan, Prabhavathi, Shanmugam K and Sugandhi Gopal, Early detection of Ischaemia Through High Frequency ECGs: The role of Medical-Grade Wearables for Chest Pain Triages (2022 IEEE International Conference on Electronics, Computing and Communication Technologies (CONECCT), Bangalore, India, 2022, pp. 1-5. https://doi.org/10.1109/CONECCT55679.2022.9865756 Cite Share Download PDF Status: Published Journal Publication published 29 Dec, 2025 Read the published version in Physical and Engineering Sciences in Medicine → Version 1 posted Reviewers agreed at journal 31 Jul, 2025 Reviewers invited by journal 31 Jul, 2025 Editor invited by journal 28 Jul, 2025 Editor assigned by journal 25 Jul, 2025 First submitted to journal 24 Jul, 2025 Editorial decision: Major revisions 03 Jul, 2025 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. 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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-6812483","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":493521305,"identity":"26d90224-df94-47aa-b090-75b22761903a","order_by":0,"name":"AISHWARYA SRINIVASAN","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA7klEQVRIie2PvwrCMBCHrwTS5bBrSv3zCpVC3eyrCIJTNxdF0KGDk7uDz5FZCdjNrkIXq9DJxUHQzdQOOqW6CeYbcly4j/sdgEbzixCgz2q1kuwgK9Y+VuwlErdQ6Ad7yhl3j5S9WgXW3MxPCFOAdLEdX8NunQLJjnuFwgR2PAQBxmo3SBu8L4NRzwtVawRSB2Etb+r5qc2JVOSPSmkJM3eKYFQqQ5vPqhVXgC8VAshC37hwUa20Bfr2yhXIcNt3DB4jJRW3NJM4Z+fRtBnE0eZy55PAMqPspDy/jAdYFFK+leNvGLdvpjUajeZveADdAjqIRMOFIgAAAABJRU5ErkJggg==","orcid":"https://orcid.org/0000-0001-5211-4606","institution":"BMS College of Engineering Department of Electronics and Communication Engineering","correspondingAuthor":true,"prefix":"","firstName":"AISHWARYA","middleName":"","lastName":"SRINIVASAN","suffix":""},{"id":493521306,"identity":"d122f54d-dd98-4daa-8b4c-00d52942d979","order_by":1,"name":"VIJAYALAKSHMI K","email":"","orcid":"","institution":"BMS College of Engineering Department of Electronics and Communication Engineering","correspondingAuthor":false,"prefix":"","firstName":"VIJAYALAKSHMI","middleName":"","lastName":"K","suffix":""},{"id":493521307,"identity":"3dfabf6f-b885-4d59-8e03-e60eca6d0994","order_by":2,"name":"SATHISH KUMAR","email":"","orcid":"","institution":"CARDITEK MEDICAL DEVICES PVT LTD, BANGALORE","correspondingAuthor":false,"prefix":"","firstName":"SATHISH","middleName":"","lastName":"KUMAR","suffix":""},{"id":493521308,"identity":"7005a181-1e2c-4bbd-9d56-37ebaaa120fd","order_by":3,"name":"POULAMI ROY","email":"","orcid":"","institution":"CARDITEK MEDICAL DEVICES PVT LTD, BANGALORE","correspondingAuthor":false,"prefix":"","firstName":"POULAMI","middleName":"","lastName":"ROY","suffix":""},{"id":493521309,"identity":"50b27007-83d5-4001-9bde-23f9f8fbf925","order_by":4,"name":"KARTHIKEYAN V J","email":"","orcid":"","institution":"Manchester University NHS Foundation Trust","correspondingAuthor":false,"prefix":"","firstName":"KARTHIKEYAN","middleName":"V","lastName":"J","suffix":""}],"badges":[],"createdAt":"2025-06-03 14:44:02","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6812483/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6812483/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s13246-025-01690-3","type":"published","date":"2025-12-29T15:58:23+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":88319964,"identity":"be8cf5be-c5f5-4f82-a3aa-58c885bed771","added_by":"auto","created_at":"2025-08-05 08:41:28","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":501303,"visible":true,"origin":"","legend":"\u003cp\u003eSchematic of the 5-Stage Analog HFECG Reference Setup. It features Ag/AgCl electrodes as precordial leads to obtain single recording from chest approximately Lead I feeding into a cascaded chain: a 100 Hz high-pass filter, a 100–500 Hz bandpass filter, a 50/60 Hz notch filter, and a final amplifier to visualize data on oscilloscope (LNA 10) scaled to match Sydäntek’s 10x output. The signals were visualized on oscilloscope but also stored in the cloud in a digitized format via a Texas Instruments ADS1298 at 1 kHz\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-6812483/v1/2e11aaf3d88793d6d83da173.png"},{"id":88320217,"identity":"115218a7-76d1-4eca-9e2d-fe6b0bdb0d68","added_by":"auto","created_at":"2025-08-05 08:49:28","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":84987,"visible":true,"origin":"","legend":"\u003cp\u003eSydäntek Digital HFECG Data Pipeline: Acquisition to Cloud Analysis Signals are captured using capacitive sensors - with a low-noise op-amp providing 10x amplification, digitized via a Texas Instruments ADS1298 at 1 kHz, and transmitted wirelessly to an Android gateway. Data is then stored in PulseVault™, a cloud database, where PulseTek™ performs signal averaging (84 beats) to enhance the signal-to-noise ratio and applies a 100–500 Hz bandpass filter for high-frequency extraction, isolating ischemia-relevant QRS components.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-6812483/v1/27bd1bbe83974e31244318c8.png"},{"id":88319984,"identity":"6462254c-1d36-4c30-b94b-b01e84a4f785","added_by":"auto","created_at":"2025-08-05 08:41:28","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":242331,"visible":true,"origin":"","legend":"\u003cp\u003eBland Altman plots from Analog and digital devices showing conformance to \u0026lt; +/- 2.5%, equivalent to +/- 95% confidence level based on standard deviation. n=84 limits of agreement are shown as green and red dotted lines. Mean as the dotted blue line, standardized to zero. 2a Altitude 2b. RMS values 2c Kurtosis\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-6812483/v1/7d07534bb61ba543805f783a.png"},{"id":88321665,"identity":"92203aaf-df85-44dc-8487-6553816d48d2","added_by":"auto","created_at":"2025-08-05 09:05:28","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":547942,"visible":true,"origin":"","legend":"\u003cp\u003eSydäntek 12-lead data of conventional ECG and HF-ECG analysis in a normal subject. The conventional ECG (displayed at 25 mm/s) shows the Signal averaged QRS complexes, while the HF-ECG (displayed at 100 mm/s) illustrates the corresponding high-frequency QRS components.\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-6812483/v1/fb09c752a7fd97802f35f33e.png"},{"id":99546155,"identity":"875b7358-2d51-48a1-a5cf-4c70dbc895bd","added_by":"auto","created_at":"2026-01-05 16:10:14","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1709735,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6812483/v1/da92d080-58d6-4c2d-b9cc-74aedd58ad54.pdf"}],"financialInterests":"","formattedTitle":"\u003cp\u003eEquivalence of Analog and Digital High-Frequency Electrocardiogram: Validating Sydäntek for Ischemia Detection\u003c/p\u003e","fulltext":[{"header":"Introduction","content":"\u003cp\u003eThe full range of useful cardiac electrical biopotentials extends from 0.01 to \u0026gt; 2000 Hz as perceived currently. As higher frequencies are examined for clinical relevance, it is apparent that\u0026nbsp;\u003c/p\u003e\n\u003col style=\"list-style-type: lower-alpha;\"\u003e\n \u003cli\u003eThe Science of ECGs had a setback because of a decision taken 100 years ago to restrict ECGs to 100 Hz\u003c/li\u003e\n \u003cli\u003eAmplitudes get decimated as frequencies rise\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eHence, very sophisticated hardware and signal processing combined with high-level math is needed to extract this. High-frequency electrocardiography (HF-ECG) analyzes ECG signals in the 100\u0026ndash;500 Hz range, capturing rapid, low-amplitude features missed by standard ECGs (0.05\u0026ndash;100 Hz) after highly specific for ischemia, indicating acute events very accurately These high-frequency components, particularly in the QRS complex, are critical for detecting myocardial ischemia and acute heart attacks. The subtle changes in ventricular activation\u0026mdash;such as reduced amplitude zones or altered morphology\u0026mdash;associated with ischemic injury were reported in the range of 150- 250 Hz[1]. HF-ECG achieves a sensitivity of over 85% for identifying ischemia, compared to 60\u0026ndash;70% for conventional ECGs, due to its ability to detect microvolt-level changes in conduction caused by impaired myocardial tissue. Pettersson et al. [3]. This makes HF-ECG a powerful tool for early diagnosis and intervention in acute coronary syndromes.\u003c/p\u003e\n\u003cp\u003eHF-ECG is accurate if there is robust signal capture and processing. Signal noise ratio is a frequent limiting factor for identifying 1 uV changes with adequate resolution- machine floor noise, muscle activity electrode motion, and resonance of PLI cause severe interference in the relevant frequencies. We use signal averaging to mitigate this and achieve adequate SNR to achieve good-quality data for high-frequency extraction. \u0026nbsp;We, on average, need approximately 30 beats to capture clean signals and also can isolate beat-by-beat HF-ECGs when the capture of ECGs is good.\u003c/p\u003e\n\u003cp\u003eBy averaging, noise is reduced by a factor of \u0026radic;N (where N is the number of beats), improving SNR by 7\u0026ndash;14 dB and enabling the detection of signals as low as 1 \u0026micro;V [2]. Traditionally, analog systems have been the benchmark for HF-ECG, employing high-fidelity amplifiers to preserve these delicate high-frequency details directly from the body [5]. However, analog setups are bulky, cumbersome, expensive, and are not suited to modern clinical needs, as in the emergency room and in the Cath lab.\u003c/p\u003e\n\u003cp\u003eOur device Syd\u0026auml;ntek, employs an all-digital approach, using high-resolution analog-to-digital converters (ADCs) and digital signal processing (DSP) to replace analog hardware. While this enhances portability and scalability, it must be proven that digital processing retains the fidelity of analog capture in the 100\u0026ndash;500 Hz range vital for ischemia detection. Any degradation\u0026mdash;due to sampling limitations, quantization noise, or filtering artifacts\u0026mdash;could undermine diagnostic reliability [4].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThus, this section demonstrates that our device\u0026rsquo;s HF- ECG output is equivalent to a traditional analog setup, using human body signals to confirm comparable amplitude, timing, and frequency content. Establishing this equivalence ensures that our digital solution upholds the clinical accuracy of analog systems, advancing accessible cardiac monitoring.\u003c/p\u003e"},{"header":"Methodology","content":"\u003cp\u003eTo confirm the equivalence of high-frequency ECG (HF-ECG) signals between a traditional analog setup and our all-digital wearable device, Syd\u0026auml;ntek, we recorded data directly from 12 human subjects, as an off-the-shelf simulator is unavailable for the range of 1 - 10 u V. This ensured real-world physiological signals in the 100\u0026ndash;500 Hz range, critical for ischemia detection, were captured under identical conditions. Recordings were performed simultaneously with consistent electrode placement (e.g., precordial leads) across both setups to eliminate temporal variability. An analog setup was custom-built as below, and our Syd\u0026auml;ntek device was used simultaneously to pick up digital signals. This study was approved by the Sri Jayadeva Ethics Committee, Bangalore, with the ethics approval number \u0026ldquo;SJICR/EC/2020/028\u0026rdquo;.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe analog setup utilized a 5-stage cascaded design to preserve HF-ECG fidelity without distortion. A single-stage bandpass filter (100\u0026ndash;500 Hz) risked phase nonlinearity and amplitude attenuation [5], so we implemented: a high-pass filter (100 Hz cutoff) to remove baseline wander, (2\u0026ndash;4) three intermediate amplifier stages with precise gain control, and a low-pass filter (500 Hz cutoff) to eliminate high-frequency noise as illustrated in Figure 1. LTspice simulations determined resistor and capacitor values for each stage, ensuring a flat frequency response and minimal phase shift across 100\u0026ndash;500 Hz. High-specification gold-topped connectors minimized contact resistance and noise, enhancing microvolt-level signal integrity. Analog signals were captured on an oscilloscope for high-resolution visualization. Initially lacking continuous recording, we took snapshots of discrete QRS complexes. Later, we enabled continuous monitoring by routing the oscilloscope output through a Texas Instruments ADS1298 development board, digitizing the signals for storage in PulseVault\u0026trade;, our cloud database.\u003c/p\u003e\n\u003cp\u003eIn contrast, Syd\u0026auml;ntek, a wearable cardiac electrical biopotential system, employed 10x capacitive sensors to acquire HF-ECG data as illustrated in Figure 2. These signals, recorded for 2 minutes, were fed into a Texas Instruments ADS1298, fully digitized into binary format, and transmitted to PulseVault\u0026trade;. Our proprietary algorithm, PulseTek\u0026trade;, processed the raw digital data, performing signal averaging across multiple QRS complexes to enhance SNR and mathematically extracting HF-ECG potentials in the 100\u0026ndash;500 Hz range. Both systems were synchronized using a common trigger pulse.\u003c/p\u003e\n\u003cp\u003eSignals from the analog setup and Syd\u0026auml;ntek were plotted independently for analysis. Across 12 subjects, 84 beats were analyzed in total. We calculated root mean square (RMS) values to assess signal magnitude and kurtosis to evaluate the distribution of high-frequency components. These metrics were compared using PulseTek\u0026trade;, alongside peak detection and Fast Fourier Transform (FFT), to quantify amplitude, timing, and frequency content equivalence. All processing and comparisons were conducted in MATLAB.\u0026nbsp;\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eHF - ECG signals from a traditional analog setup and Syd\u0026auml;ntek, an all-digital wearable system from Carditek Medical Devices, were compared across 12 healthy subjects (84 beats total) in the 100\u0026ndash;500 Hz range to assess equivalence for ischemia detection. Signals were recorded simultaneously using a 5-stage analog system (100\u0026ndash;500 Hz bandpass, gold connectors) and Syd\u0026auml;ntek\u0026rsquo;s 10x capacitive sensors, both digitized via a Texas Instruments ADS1298 at 1 kHz. All signals underwent 10x amplification via Syd\u0026auml;ntek\u0026rsquo;s low-noise op-amp, with the analog output scaled to match for direct comparison; values reported here reflect actual pre-amplified measurements in microvolts (\u0026micro;V). Processed by PulseTek\u0026trade; and stored in PulseVault\u0026trade;, signals were analyzed for amplitude, root mean square (RMS), kurtosis, and frequency content. Bland-Altman analysis, with analog as the standard, quantified agreement between methods.\u003c/p\u003e\n\u003cp\u003eFor RMS, the mean difference (analog minus Syd\u0026auml;ntek) was 6.39 \u0026micro;V, with a standard deviation (SD) of 28.64 \u0026micro;V, yielding 95% limits of agreement (LOA) from -49.74 to 62.52 \u0026micro;V. Amplitude measurements showed a mean difference of 1.82 \u0026micro;V (SD: 30.05 \u0026micro;V), with LOA from -57.09 to 60.73 \u0026micro;V. Kurtosis, a unitless measure of signal distribution shape, had a mean difference of 1.93 (SD: 1.70), with LOA from -5.13 to 1.54. Frequency content, derived via Fast Fourier Transform (FFT), exhibited a mean difference of 2.1 Hz (SD: 3.06 Hz), with LOA from -5.8 to 6.2 Hz, and both systems peaked at approximately 150 Hz. When scaled to the 10x amplified output (e.g., RMS: 63.9 \u0026micro;V, LOA: -497.4 to 625.2 \u0026micro;V; amplitude: 18.2 \u0026micro;V, LOA: -570.9 to 607.3 \u0026micro;V), differences remained within 5% (+/-2.5 %) of typical QRS amplitudes (1\u0026ndash;2 mV pre-amplified, 10\u0026ndash;20 mV amplified), as visualized in Bland-Altman plots (e.g., Figure 3). These results demonstrate Syd\u0026auml;ntek\u0026rsquo;s close alignment with analog HF-ECG signals across all metrics, supporting its equivalence in capturing ischemia-relevant features\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study validates the equivalence of Syd\u0026auml;ntek, a digital wearable HF-ECG system, to a traditional analog setup, leveraging updated Bland-Altman statistics that reflect improved measurement precision. The mean differences\u0026mdash;RMS: 6.39 \u0026micro;V, amplitude: 1.82 \u0026micro;V, kurtosis: 1.93, and frequency: 2.1 Hz\u0026mdash;are notably small relative to typical HF-ECG signal magnitudes (e.g., QRS amplitudes of 1000\u0026ndash;2000 \u0026micro;V pre-amplified), with 95% LOA indicating tight agreement (e.g., RMS: -49.74 to 62.52 \u0026micro;V; amplitude: -57.09 to 60.73 \u0026micro;V). These values, when scaled by Syd\u0026auml;ntek\u0026rsquo;s 10x amplification (e.g., RMS difference: 63.9 \u0026micro;V), remain within a clinically acceptable 5% tolerance of amplified QRS signals (10\u0026ndash;20 mV), aligning with prior benchmarks for HF-ECG equivalence [5]. The minimal bias (e.g., 1.82 \u0026micro;V for amplitude) suggests Syd\u0026auml;ntek faithfully reproduces analog signal characteristics, while the slightly wider LOA (e.g., \u0026plusmn;\u0026thinsp;60 \u0026micro;V for amplitude) reflects natural variability across 84 beats rather than systematic error.\u003c/p\u003e\u003cp\u003eThe consistency in frequency content (2.1 Hz difference, peaking at ~\u0026thinsp;150 Hz) reinforces Syd\u0026auml;ntek\u0026rsquo;s ability to capture the 150\u0026ndash;250 Hz bandwidth critical for ischemia detection, as established by Abboud et al. [1]. This bandwidth, where Reduced Amplitude Zones (RAZ) and other ischemia markers manifest, is preserved across both systems, with Syd\u0026auml;ntek\u0026rsquo;s digital processing 9 (6.39 \u0026micro;V vs. 54.5 \u0026micro;V for RMS) and tight LOA reflect strong calibration and signal processing, likely from PulseTek\u0026trade; 's optimization, reflecting Syd\u0026auml;ntek\u0026rsquo;s equivalence. This aligns with Schlegel et al. [4], who demonstrated digital HF-ECG \u0026rsquo;s potential to exceed analog limitations (e.g., noise from connectors), though our analog setup\u0026rsquo;s gold-standard design minimized such artifacts.\u003c/p\u003e\u003cp\u003eClinically, Syd\u0026auml;ntek\u0026rsquo;s equivalence to analog HF-ECG, combined with its wearable form factor and cloud integration via PulseVault\u0026trade;, positions it as a transformative tool for ischemia monitoring. Traditional analog systems, while reliable, lack portability and real-time data storage, limitations Syd\u0026auml;ntek overcomes without sacrificing accuracy. The 5% tolerance threshold ensures that differences (e.g., 6.39 \u0026micro;V RMS) are negligible against the microvolt-level changes (50\u0026ndash;100 \u0026micro;V) indicative of ischemia [3]. However, this study\u0026rsquo;s small sample (12 subjects) and healthy cohort limit generalizability to ischemic patients, necessitating larger trials like the subsequent SEES Trial (CTRI/2021/04/032733) to confirm diagnostic utility.\u003c/p\u003e\u003cp\u003eIn conclusion, Syd\u0026auml;ntek matches analog HF-ECG performance, validated by precise Bland-Altman metrics, offering a portable, scalable alternative for ischemia detection. These findings pave the way for standardized HF-ECG norms, as explored in our follow-up registry, enhancing global cardiac care.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis Equivalence study confirms that Syd\u0026auml;ntek\u0026rsquo;s all-digital, wearable HF-ECG platform replicates the fidelity of a high-grade analog system in capturing microvolt-level signals within the 100\u0026ndash;500 Hz range. Through direct physiological recordings, synchronized acquisition, and rigorous statistical comparisons, the device demonstrated equivalence in waveform morphology, amplitude, and spectral characteristics, all of which are critical for ischemia detection. By integrating capacitive sensing, signal averaging, and cloud-native processing via PulseTek\u0026trade; and PulseVault\u0026trade;, Syd\u0026auml;ntek offers a portable, scalable alternative to analog setups, ready for deployment in clinical and emergency care settings.\u003c/p\u003e\u003cp\u003e\u003cb\u003eClinical Implications\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eSyd\u0026auml;ntek\u0026rsquo;s validated equivalence to analog HF-ECG holds significant promise for ischemia diagnosis, where standard ECGs miss up to 50% of occlusions [3]. With differences (e.g., 63.9 \u0026micro;V RMS amplified) far below ischemia markers (50\u0026ndash;100 \u0026micro;V), Syd\u0026auml;ntek ensures reliable detection of subtle QRS changes in a wearable format. Figure\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e4\u003c/span\u003e showcases its potential in 12-lead HF-ECG, offering a glimpse of improved sensitivity and specificity over traditional 12-lead ECGs [6], as validated here and poised for expansion in larger cohorts. This stepping stone supports real-time, accessible cardiac monitoring, potentially reducing the 100,000\u0026ndash;500,000 annual missed Myocardial infarct post-emergency room discharge [3], and advancing precision cardiology globally.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors sincerely thank Carditek Medical Devices Pvt. Ltd., Bangalore, for providing the resources, and technical support essential for conducting this research. This research was also supported by the National Biopharma Mission Grant (NBM) through the Biotechnology Industry Research Assistance Council (BIRAC).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflicts of Interest - \u003c/strong\u003eAll authors declare that they have no conflicts of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval - \u003c/strong\u003eThis is a validation study performed under the approval of Sri Jayadeva Ethics Committee, Bangalore\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to participate-\u003c/strong\u003e Informed consent was obtained from all individual participants included in the study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions \u003c/strong\u003e- All authors contributed to the study\u0026apos;s conception, design, manuscript and approved the final version.\u003c/p\u003e"},{"header":"References","content":"\u003col class=\"decimal_type\"\u003e\n\u003cli\u003eVictor Mor-Avi, B. Shargorodsky, S. Abboud, S. Laniado, and S. Akselrod (1987) Effects of coronary occlusion on high-frequency content of the epicardial electrogram and body surface electrocardiogram. Circulation, vol. 76, no. 1, pp. 237\u0026ndash;243. https://doi.org/10.1161/01.cir.76.1.237\u003c/li\u003e\n\u003cli\u003eVictor Mor-Avi and Shlomo Akselrod (1990) Some aspects of the wideband recording of the electrocardiogram. Clinical Cardiology, vol. 13, no. 2, pp. 120\u0026ndash;126. https://doi.org/10.1002/clc.4960130211 \u003c/li\u003e\n\u003cli\u003eJonas Pettersson, Olle Pahlm, Eduardo Carro, Lars Edenbrandt, Mats Ringborn, Leif S\u0026ouml;rnmo, Steven G. Warren, and Galen S. Wagner (2000) Changes in high-frequency QRS components are more sensitive than ST-segment deviation for detecting acute coronary artery occlusion. Journal of the American College of Cardiology, vol. 36, no. 6, pp. 1827\u0026ndash;1834. https://doi.org/10.1016/S0735-1097(00)00947-8\u003c/li\u003e\n\u003cli\u003eThomas T. Schlegel, Wieslaw B. Kulecz, John L. DePalma, Andrew H. Feiveson, James S. Wilson, Mohammad A. Rahman, and Michael W. Bungo (2004) Real-time 12-lead high-frequency QRS electrocardiography for enhanced detection of myocardial ischemia and coronary artery disease. Mayo Clinic Proceedings, vol. 79, no. 3, pp. 339\u0026ndash;350. https://doi.org/10.4065/79.3.339\u003c/li\u003e\n\u003cli\u003eEmma Tr\u0026auml;g\u0026aring;rdh, Thomas T. Schlegel, and Olle Pahlm (2007) High-frequency electrocardiogram,\u0026quot; Clinical Physiology and Functional Imaging, vol. 27, no. 4, pp. 210\u0026ndash;216. https://doi.org/10.1111/j.1475-097X.2007.00738.x\u003c/li\u003e\n\u003cli\u003eAishwarya Srinivasan, Vijayalakshmi K, Deepak Padmanabhan, Prabhavathi, Shanmugam K and Sugandhi Gopal, Early detection of Ischaemia Through High Frequency ECGs: The role of Medical-Grade Wearables for Chest Pain Triages (2022 IEEE International Conference on Electronics, Computing and Communication Technologies (CONECCT), Bangalore, India, 2022, pp. 1-5. https://doi.org/10.1109/CONECCT55679.2022.9865756 \u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":true,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"physical-and-engineering-sciences-in-medicine","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"apes","sideBox":"Learn more about [Physical and Engineering Sciences in Medicine](http://link.springer.com/journal/13246)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/apes/default.aspx","title":"Physical and Engineering Sciences in Medicine","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"High frequency ECG (HF-ECG), Ischemia Detection, Capacitive Sensors, Microvolt-level ECG, Cloud-integrated ECG monitoring, Digital Bio-marker validation","lastPublishedDoi":"10.21203/rs.3.rs-6812483/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6812483/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eHigh-frequency electrocardiography (HF-ECG) in the 100\u0026ndash;500 Hz range enhances ischemia detection by capturing microvolt-level QRS changes, yet its clinical adoption requires the validation of digital systems against analog standards. HF-ECG signals were recorded simultaneously from 12 healthy subjects (84 beats total) using a 5-stage analog system (100\u0026ndash;500 Hz bandpass, gold connectors) and Syd\u0026auml;ntek\u0026rsquo;s 10x capacitive sensors, both digitized via Texas Instruments ADS1298. Signals underwent 10x amplification (low-noise op-amp), with analog scaled to match, and were processed by PulseTek\u0026trade; and stored in PulseVault\u0026trade;. Amplitude, root mean square (RMS), kurtosis, and frequency content were analyzed using Bland-Altman methods, with analog as the standard; values reflect pre-amplified measurements in microvolts (\u0026micro;V). Mean differences (analog minus Syd\u0026auml;ntek) were minimal\u0026mdash;RMS: 6.39 \u0026micro;V (95% LOA: -49.74 to 62.52 \u0026micro;V), amplitude: 1.82 \u0026micro;V (-57.09 to 60.73 \u0026micro;V), kurtosis: 1.93 (-5.13 to 1.54), frequency: 2.1 Hz (-5.8 to 6.2 Hz)\u0026mdash;all within 5% clinical tolerance when scaled 10x (~\u0026thinsp;10\u0026ndash;20 mV). Syd\u0026auml;ntek matched analog fidelity, with frequency peaks at ~\u0026thinsp;150 Hz. Syd\u0026auml;ntek\u0026rsquo;s digital HF-ECG performance is equivalent to analog systems, validated by tight agreement in key metrics. Its wearable design and cloud integration via PulseVault\u0026trade; and PulseTek\u0026trade; offer a portable, reliable alternative for ischemia detection, supporting broader clinical applications.\u003c/p\u003e","manuscriptTitle":"Equivalence of Analog and Digital High-Frequency Electrocardiogram: Validating Sydäntek for Ischemia Detection","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-08-05 08:41:23","doi":"10.21203/rs.3.rs-6812483/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"","date":"2025-07-31T07:01:43+00:00","index":0,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-07-31T05:40:25+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"Physical and Engineering Sciences in Medicine","date":"2025-07-28T23:29:58+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-07-25T13:25:46+00:00","index":"","fulltext":""},{"type":"submitted","content":"Physical and Engineering Sciences in Medicine","date":"2025-07-24T12:30:21+00:00","index":"","fulltext":""},{"type":"decision","content":"Major revisions","date":"2025-07-03T04:29:09+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"physical-and-engineering-sciences-in-medicine","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"apes","sideBox":"Learn more about [Physical and Engineering Sciences in Medicine](http://link.springer.com/journal/13246)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/apes/default.aspx","title":"Physical and Engineering Sciences in Medicine","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"4dec65b1-516f-4b6f-8cb0-6ac0d1ba1b1c","owner":[],"postedDate":"August 5th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2026-01-05T16:08:34+00:00","versionOfRecord":{"articleIdentity":"rs-6812483","link":"https://doi.org/10.1007/s13246-025-01690-3","journal":{"identity":"physical-and-engineering-sciences-in-medicine","isVorOnly":false,"title":"Physical and Engineering Sciences in Medicine"},"publishedOn":"2025-12-29 15:58:23","publishedOnDateReadable":"December 29th, 2025"},"versionCreatedAt":"2025-08-05 08:41:23","video":"","vorDoi":"10.1007/s13246-025-01690-3","vorDoiUrl":"https://doi.org/10.1007/s13246-025-01690-3","workflowStages":[]},"version":"v1","identity":"rs-6812483","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6812483","identity":"rs-6812483","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}