The use of heart rate variability (HRV) biomarkers in the identification of anger in adults

The use of heart rate variability (HRV) biomarkers in the identification of anger in adults | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article The use of heart rate variability (HRV) biomarkers in the identification of anger in adults Zahra Dehghanizadeh, Behrouz Dolatshahee, Masoud Nosratabadi, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3965051/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 Background Undoubtedly, the existence of accurate measurement tools can be an important step in the direction of early diagnosis of problems related to emotions and emotional dysregulation. Objective I n the present study, we tried to identify the emotion of anger in people using one of the biomarkers called heart rate variability (HRV). In this way, the diagnostic accuracy of five subscales related to heart rate, i.e. HR, RR, HF, LF and LFtoHF, was investigated among subjects with high anger and low anger. Method The study is descriptive (exploratory) and quantitative analysis. The current research population includes all adults (age range 20–45 years selected based on research literature) living in Tehran. The statistical sample of the current research, which was selected using the available sampling method based on the entry and exit criteria, was 100 people who, after the initial review of the data obtained from their heart rate sensor and preparation for entering the analysis stage, due to insufficient recording time and Lack of accurate recording of heart rate eliminated 24 of them and the remaining sample in the present study was 76. The instrument of the current research consists of two parts: Panas positive and negative emotions scale and BVP sensor. All analyzes related to descriptive statistics, t table of two independent groups were performed by SPSS software. In addition, ROC analysis and sensitivity analysis were performed by Medcalc software. Results Preliminary studies on the difference in the mean scores of the two groups of people with high and low anger in five scales HR, RR, HF, LF and LFtoHF showed that the difference in the mean scores of the two groups in the HR scale (at a significant level of less than 0.05) and RR (at a significance level of less than 0.001) is statistically significant. In both scales, the average scores of people with high anger were higher than the average scores of people with low anger. Among the five HR, RR, HF, LF and LFtoHF scales, the RR scale had the highest purity index (= 0.71). This scale performed significantly better than other HR, HF, LF and LFtoHF scales. Conclusion The results of this study can make the use of biological indicators more prominent in identifying positive and negative emotions. heart rate variability biomarkers anger emotion dysregulation Figures Figure 1 Figure 2 Introduction Humans view the world through a perceptual and emotional filter that is formed by a wide and diverse range of personality-oriented thoughts and behaviors (1, 2). Contemporary researchers consider emotions as the foundation of human growth and relationships(3, 4). Since emotions are social in nature, they are a basic means of communication for humans. Unfortunately, there are people who don't know what their emotions are (they don't know their emotions), let alone how to express them(5, 6). Emotion is often defined as a complex feeling that results in physical and psychological changes that affect thought and behavior(7, 8). Despite the fact that psychologists have paid much attention to emotions in recent years, few clinical and objective recommendations have been provided to effectively deal with specific emotions(9). In fact, if we can accurately identify emotions and measure them objectively, we can greatly help reduce emotional problems and increase people's mental health. Therefore, the methods of identifying and evaluating emotions presented in the research literature can be classified into two main groups according to the basic techniques used in the detection of emotions: based on self-evaluation of emotions by completing different questionnaires(10, 11). Machine evaluation techniques based on measuring different parameters of the human body (12, 13). It is difficult to assess emotions in clinical settings, which must be obtained by periodic self-reports by patients. Self-reporting always depends on people's willingness to tell and is subject to intra-subject variation in reporting due to other contextual factors such as environment, people, situation, etc(14). However, one of the most distinctive of these ways is the monitoring of human physiological changes. There are signals that the human body shows involuntarily, such as heart rate variability, skin/muscle tension, facial expressions, pupil dilation, and micro-movements. Most of them have a bio-electric shock which in turn can be recorded by sensors(15). Using biofeedback, these signals can identify and regulate emotion, which has been widely investigated in many studies(16, 17). Considering the large number of available biological signals, one of the problems is choosing the most appropriate biological signals, which are used as a method to convert signals into emotions(18). Therefore, it is very important to use suitable sensors to measure biosignals, and high precision is required to obtain good reliability(19, 20). For automatic detection and evaluation, most emotion evaluation studies focus on other classifications (21, 22) which includes the dimensions of emotions, in most cases capacity (activation-negative/positive) and arousal (high/low) (23–25) And it analyzes only the basic emotions such as anger, fear, sadness, happiness, surprise, disgust and contempt, which can be easily defined and provides an assessment of their intensity. Among the different emotions that we experience, anger has the most negative effect on human health (26–28) Therefore, this issue may become a complicated issue for the individual, in which case, the need for medical help to deal with it is necessary(28, 29) On the other hand, continuous expression of anger and anger and inability to control it may also lead to failure in social relations and interpersonal interactions(26, 29) Accordingly, the most important benefit of being aware of and paying attention to angry feelings is the opportunity we get to regulate or inhibit our reactions, reassess the situation, and plan actions that are most likely to remove the source of the anger(30–32) The ability to recognize emotions early can help us deal with people in various situations and manage our emotional responses to their feelings. Furthermore, the work we do to develop impulse awareness can be beneficial for what almost all of us can achieve—being aware of emotional behavior, or recognizing our emotional state as soon as it begins to express itself in words or actions(27, 33) If you can become aware that an emotion has started to guide your behavior, you can consciously check whether your emotional reaction is appropriate for the situation you are in, and if so, whether it has the right intensity and express yourself in the most constructive way. does or not(30, 34) Recently, authors extracted features from facial expressions and EEG of 27 subjects. They combined these methods and obtained the highest accuracies of 66.28% and 63.22% for excitation using decision-level fusion.(35) Also, other authors(36) collected RSP, EMG and SC from 20 subjects. They obtained the highest detection rate of 80% for arousal and 76.67% for capacity by combining these three methods. When the 4 categories were arbitrarily adjusted to account for the arousal and valence dimensions, the highest accuracy of 50% was achieved with single-modality RSP or EMG rather than modality fusion. In the well-documented literature, "individual differences" have been raised as a matter of widespread concern. Humans may show different emotions for the same emotion and have different physiological patterns when exposed to an emotional stimulus. This issue was first raised by Pickard et al. The main motive of using physiological signals as opposed to subjective personal reports in the detection of emotions was to discover the internal connection of signal patterns with human emotional states and finally to accurately detect emotions (37, 38). Valenza et al. (2012) suggested that heart rate variability (HRV) can be an objective tool to assess emotional responses (2, 39, 40). Also, Lin et al. (2009) reported the relationship between the subject's emotional state and HRV (4). Although HRV is now used sporadically in experiments designed to assess human emotional states, no research has yet confirmed its validity as a tool for assessing human emotion(6, 8) In the present study, the goal of the researchers is to be able to identify anger using the heart rate sensor - BVP. This research is the first phase of a research based on the design of an emotional smart wristband that is able to identify emotions in real time and give feedback to the person so that he is aware of his emotional state and type of emotion and then he can deal with the situation appropriately. Among the strategies of emotion regulation, show that it leads to better and more adaptation. Therefore, the obtained diagnostic accuracy helps us to know if heart rate can be the right tool to evaluate and detect emotions in real time? Method The study is descriptive (exploratory) and quantitative analysis. The current research population includes all adults (age range 20–45 years) living in Tehran. The statistical sample of the current research, which was selected using the available sampling method based on the entry and exit criteria, was 100 people who, after the initial review of the data obtained from their heart rate sensor and preparation for entering the analysis stage, due to insufficient recording time and Lack of accurate recording of heart rate eliminated 24 of them and the remaining sample in the present study was 76. The entry criteria include agreeing to participate in the project and use the BVP sensor, age range of 20–45 years, living in Tehran, not suffering from heart diseases, blood pressure and blood phobia. Exclusion criteria include discontinuing participation in the research or developing an illness that restricts the person from participating in the research. In the first stage, among the 20 films approved in scientific articles, 3 films that induced the emotion of anger were selected. In fact, 5-minute pieces of films were chosen, in which moments of anger could be clearly perceived. In the next step, volunteers were invited to participate in the research to perform the test steps. The experiment was carried out in an emotion induction phase by showing a film using anger content on the TV screen of Parand Clinic's testing room. At this stage, the heart rate of each subject was recorded by the BVP sensor for 6 minutes. At the task completion stage, fragments of all three films were shown and immediately after the completion of the film, the Panas positive and negative emotions scale prepared online was answered by the subject about the quantity and quality of the emotions he perceived. In fact, following the self-reports of the first 10 subjects using a single subject method, the movie My Bodyguard was finally selected for the first phase of the research due to the better induction of anger. In the following, 100 volunteers also participated in the emotional induction phase, and the biographic data of 76 of them were confirmed for analysis. Instrument Panas Positive and Negative Emotions Scale: To measure concurrent validity, the Panas Positive and Negative Emotions Scale prepared and presented by Watson, Clark and Telgen in 1988 will be used. This scale measures 20 items (10 positive feelings and 10 negative feelings) in the majority of words and is generally evaluated on a five-point Likert scale (from strongly agree to strongly disagree). Cronbach's alpha coefficient for this scale was reported as 0.85 in the research of Sohrabi and Hosseini (1381) and Abolghasemi (1383) obtained the internal correlation coefficient of the components in the whole scale between 0.74 and 0.94, all of which were at the level It was significant less than 0.01 and indicates the validity of this structure. Also, in the research of Shiroudi and Ghorbani (2013), the results of a factor analysis for the standardization of PANAS positive and negative emotional scale, which was carried out on 420 students, showed that the reliability coefficient of the tool, which was estimated based on the general formula of Cronbach's alpha, was 0.87. Therefore, this tool can be used in Iranian society (20) BVP sensor: BVP sensor and Bioline biofeedback device (manufactured by Madinatab, Iran) were used to record heart rate. HRV is an emotional state assessment technique based on the measurement of heart rate variability, which means beat-to-beat changes over time during a given period of sinus rhythm. Unlike average heart rate variance, which is expressed over a 60-second period, HRV analysis examines the fine-grained time variance within each heart rate cycle and its regularity (22). The change in heart rate is regulated by the synergistic action of the two branches of the autonomic nervous system, i.e. the sympathetic and parasympathetic nervous systems. The heart rate reflects the net effect of the parasympathetic nerves, which slow the heart rate, and the sympathetic nerves, which speed it up. These changes are influenced by emotions, stress and physical exercises. In addition, HRV depends on age and gender, and additional factors include physical and psychological stress, smoking, alcohol, coffee, overweight, and blood pressure, as well as glucose levels, infectious agents, and depression (25) Data analysis By implementing the scales, collecting data and recording them in the computer, it became possible to analyze the data. All analyzes related to descriptive statistics, t table of two independent groups were performed by SPSS software. In addition, ROC analysis and sensitivity analysis were performed by Medcalc software. Results Descriptive statics The results related to mean, standard deviation, skewness and stretching of five scales HR, RR, LF, HF, LFtoHF-TASK are shown in Table 1 . As can be seen, the values of skewness and elongation of HR, skewness in RR and skewness and elongation in LFtoHF scale are less than (16), therefore, citing Tabachinek and Fidel (1996), it can be concluded that the distribution of scores of the three scales is relatively normal. and they have no problem in terms of curvature and tension. Table 1 The results of descriptive statistics for five scales HR, RR, LF, HF, LftoHF Scales Mean SD Skeweness Kurtosis HR 81.46 9.32 -0.19 -0.75 RR 754.90 108.04 -1.22 7.15 LF 1592.78 3017.16 4.05 17.24 HF 3154.59 5651.46 2.68 6.63 LFTOHF 0.79 0.56 1.49 1.88 The results of the t test are shown in Table 2 . Independent groups t-test was used to examine the difference between two groups of high anger and low anger in each scale of HR, RR, LF, HF, LFtoHF. Table 2 The results of t test (t) of independent groups خطای استاندارد برآورد تفاوت تفاوت میانگین ها سطح معنی داری t df مقیاس ها 17/2 57/5 012/0 56/2 74 HR 83/24 40/74- 004/0 99/2- 74 RR 45/725 35/984- 17/0 35/1- 74 LF 14/1357 19/1935- 15/0 42/1- 74 HF 13/0 17/0 19/0 29/1 74 LFtoHF As can be seen, there is a significant difference between the scores of the two groups in the HR and RR scales. However, no significant difference was observed in the other three scales. The resulting t-statistic about the HR scale was statistically significant (p < 0.05 and t = (76) 2.56). Therefore, there is a significant difference between the two groups of high anger and low anger in this scale. The resulting t-statistic about the RR scale was statistically significant (p < 0.05 and t = -2.99 (76)). Therefore, there is a significant difference between the two groups of high anger and low anger in this scale. d) Examining the diagnostic accuracy of the scales ROC analysis and sensitivity analysis were used to investigate the three scales HR, RR, LF, HF, and LFtoHF. The results of ROC analysis are presented in Table 3 . As it is clear in the table, the RR scale has the highest value of cleanness index ( ) compared to other scales (= 0.71) and after that the HR scale has the highest value of cleanness index (= 0.69). But the RR scale has a significantly better performance than the other scale, namely HR (Z = 0.01 and Z = 0.06). This neat index shows that the RR scale is able to correctly classify people into two groups of people with high aggression and low aggression in 71% of cases. Although the HR scale did not obtain a higher purity index (0.69), it has a better performance than other scales (Z = 0.06 and 0.01). The rest of the scales i.e. HF, LF, LFtoHF do not have good performance and clinical value. Table 3 The results of ROC analysis for two groups of people with high and low aggression Scales A Z SE CI RR 0.71 0.06 0.60–0.81 HR 0.69 0.06 0.57–0.79 HF 0.59 0.07 0.47–0.70 LF 0.54 0.06 0.42–0.66 LFtoHF 0.55 0.07 0.43–0.66 As can be seen in Table 3 , the level under the curve of the RR scale is 71%, as mentioned above. The only scale that can identify the emotion of anger in people with a probability of 71% and with a confidence interval (0.81 − 0.60) (P = 0.001). In the following chart, this clean index is shown in more detail and in comparison with other scales. For this graph, we chose the categorized variable of anger and all the heart rate variables including HR-TASK, RR-TASK, LF-TASK, HF-TASK, LFtoHF-TASK and rock analysis showed that RR-TASK The rest of the variables are more significant.Therefore, we check the RR scale separately in the chart below, and its values are also reported. As seen in Fig. 2 , the area under the ROC curve for the RR scale is 71% (P = 0.001). This means that the heart rate RR scale can detect anger in highly aggressive individuals with a probability of 71%. The sensitivity and specificity are also explained in the following tables. In order to determine the optimal cut score for five scales RR, HR, HF, LF, LFtoHF, sensitivity analysis was used. In sensitivity analysis, several characteristics are considered, which include sensitivity, specificity, positive likelihood ratio and negative likelihood ratio. Of course, sometimes, instead of positive probability ratio and negative probability ratio, two indicators of false positive rate and false negative rate are considered. A suitable cut-off score is a test score that can optimize the value of the mentioned 4 characteristics. Among the 4 mentioned characteristics, sensitivity is very important. The optimal cut-off score is a score that, while keeping the sensitivity and specificity values at their maximum value, keeps the negative probability ratio at its minimum value. Therefore, in a wide range of cut-off points (examination scores), only one score represents the desired cut-off score that can maintain the mentioned state. It is also important to mention this point that regardless of the cut-off score provided, this cut-off score is effective when the scale has a good purity index . Table 4 Area under the ROC curve for the RR scale Area under the ROC curve (AUC) SE CI Z sig 0.71 0.06 0.60–0.81 3.20 0.001 جدول 5: شاخص Youden برای مقیاس RR Youden index Cut point Sensivity score Characteristic score 0.45 88 66/690< 0.45 As stated before, in the current research, only RR has a favorable and relatively good cleanliness index. Therefore, according to the total of two ROC and sensitivity analyses, it can be concluded that if a person scores higher than 690.66 in the RR scale, there is a 71% probability that he has anger and aggressive behaviors. These results are favorable compared to previous similar studies and this shows that the use of a heart rate sensor can be a suitable and reliable tool to detect and identify the emotion of anger. In order to determine the optimal cutoff, we are faced with an index called the Youden index. Based on this index, our optimal and desirable cut-off point is the one with the highest sum of sensitivity and specificity. The accuracy in the above results shows that the number 690/66 with sensitivity equal to 88.00% can be considered as the best cut point. Conclusion Undoubtedly, the existence of accurate measurement tools can be an important step in the direction of early diagnosis of problems related to emotions and emotional dysregulation. In the present study, we tried to identify the emotion of anger in people using one of the biomarkers called heart rate variability (HRV). In this way, the diagnostic accuracy of five subscales related to heart rate, i.e. HR, RR, HF, LF and LFtoHF, was investigated among subjects with high anger and low anger. Descriptive calculations in order to investigate the distribution of people's scores in these five scales showed that all five scales have relatively normal distribution and do not have any problems in terms of skewness and stretching. Preliminary studies on the difference between the mean scores of two groups of people with high and low anger in five scales HR, RR, HF, LF and LFtoHF showed that the difference between the mean scores of the two groups in the HR scale (at a significant level of less than 0.05) and RR (at a significance level of less than 0.001) is statistically significant. In both scales, the average scores of people with high anger were higher than the average of people with low anger. Determining the optimal cut-off score for these scales based on the observed differences, in addition to being associated with many errors, does not have strong statistical support; Therefore, ROC and sensitivity analysis were used to determine the optimal cut-off score and also to examine the diagnostic accuracy of these scales. Among the five HR, RR, HF, LF and LFtoHF scales, the RR scale had the highest purity index (= 0.71). This scale performed significantly better than other HR, HF, LF and LFtoHF scales. Since in clinical works, the purity index ( ) above 0.70 is usually considered as a relatively good and desirable index, therefore, in the present study, only the purity index of the RR scale is clinically acceptable. The purity index value of HR scale (= 0.69) is also less than 0.71, clinically it is not a desirable index, but it performs better than other scales. Therefore, it can be concluded that based on the ROC analysis, the RR scale is able to correctly classify people into two groups of people with high anger and people with low anger in 71% of cases, and HR scale in 69% of cases These findings are in line with the research of Chang et al. (2021) who distinguished happiness and sadness with the help of artificial intelligence using HRV. The results of their research showed that these emotions can be identified by HRV with a sensitivity of 70.7% and a specificity of 58.4%. Also, with studies such as Chen et al. (2017) who in China obtained the average accuracy of emotional intelligent device detection of 77.57% in detecting four emotions with the best accuracy of 86.67% for positive emotion and excited emotion detection (37), Haag et al. 21) In a research in Germany, researchers reached a diagnostic accuracy of 89.90% in the capacity dimension and 96.60% in the arousal dimension, and Wagner et al. They reached 92.05% accuracy to detect four emotions, it is consistent. Recently, Huang et al. (37) extracted features from facial expressions and EEG of 27 subjects. They combined these methods and obtained the highest accuracy of 66.28% for capacity. Finally, this research showed that HRV can be used as an objective method to identify emotions. Also, we emphasize that while the use of biosignals is a valuable tool for emotion recognition, there is currently a question as to whether it alone is sufficient. It seems that combining different sources on top of biosensor signals, such as video analysis, motion detection or emotion recognition from speech, is a necessary step to avoid the limitations of single-modality systems. In the present study, due to the small size of the sample, the results cannot be attributed to all people with certainty. Therefore, future researches should clarify the validity and reliability of these findings (with a larger sample size and in other groups). 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Rahmani M, Mahvelati A, Farajinia AH, Shahyad S, Khaksarian M, Nooripour R, et al. Comparison of Vitamin D, Neurofeedback, and Neurofeedback Combined with Vitamin D Supplementation in Children with Attention-Deficit/Hyperactivity Disorder. Archives of Iranian Medicine. 2022;25(5):285–393. 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-3965051","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":273690299,"identity":"3d8aaec2-d0f9-4bf2-be78-0da9f40765fa","order_by":0,"name":"Zahra Dehghanizadeh","email":"","orcid":"","institution":"University of Social Welfare and Rehabilitation Sciences","correspondingAuthor":false,"prefix":"","firstName":"Zahra","middleName":"","lastName":"Dehghanizadeh","suffix":""},{"id":273690300,"identity":"6f4f6efe-beeb-4c9a-b588-e00ba13d01ac","order_by":1,"name":"Behrouz Dolatshahee","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA5UlEQVRIiWNgGAWjYDCCA8wNIJLBgIGB8QGIQYQWxgagOrAWZgOStbBJEKWF73hj4+cPf+7Im7M3H6vmqbkjx8/A/PDRDTxaJM8cbJY42PbMcGfPsbTbPMeeGUs2sBkb5+DRYnAjsUHiYMNhxg03csxu87AdTtxwgIdNmoCW5h8H/hy233D//bdinn/EaWmTOAAy/AYPGzNvGxFagH5pszjbdjh5w5k0Y8m5fYeNJZsJ+IXvePPhGxV/DttuOH744Yc33w7L8bM3P3yMTwsKYOIBkczEKgcBxh+kqB4Fo2AUjIIRAwB7RF6xE9SQzwAAAABJRU5ErkJggg==","orcid":"","institution":"University of Social Welfare and Rehabilitation Sciences","correspondingAuthor":true,"prefix":"","firstName":"Behrouz","middleName":"","lastName":"Dolatshahee","suffix":""},{"id":273690301,"identity":"d5ba7913-2660-4217-bf66-167ac6942ff5","order_by":2,"name":"Masoud Nosratabadi","email":"","orcid":"","institution":"University of Social Welfare and Rehabilitation Sciences","correspondingAuthor":false,"prefix":"","firstName":"Masoud","middleName":"","lastName":"Nosratabadi","suffix":""},{"id":273690302,"identity":"66304baa-48a4-4ac8-ab74-ebd782a20f53","order_by":3,"name":"Manocher Moradi Sabzevar","email":"","orcid":"","institution":"University of Tehran","correspondingAuthor":false,"prefix":"","firstName":"Manocher","middleName":"Moradi","lastName":"Sabzevar","suffix":""}],"badges":[],"createdAt":"2024-02-17 21:02:54","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3965051/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3965051/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":51449967,"identity":"43da2240-703d-40cf-80c2-2cda14a067c8","added_by":"auto","created_at":"2024-02-21 20:16:15","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":201723,"visible":true,"origin":"","legend":"\u003cp\u003eROC curves diagram for five scales RR, HR, HF, LF, LFtoHF\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-3965051/v1/0a16f698c8ce66802ac4ddeb.png"},{"id":51449968,"identity":"5c62bc67-d686-415f-8a5b-98f0c4531842","added_by":"auto","created_at":"2024-02-21 20:16:15","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":139244,"visible":true,"origin":"","legend":"\u003cp\u003eROC curve diagram for RR scale\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-3965051/v1/f89c406fecb84076484aaa43.png"},{"id":62555429,"identity":"6033da25-a499-4403-835a-cdaf03401cc9","added_by":"auto","created_at":"2024-08-15 19:07:32","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":611285,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3965051/v1/dfb299b6-519d-4e8a-bad8-5f361bf6a1b7.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"The use of heart rate variability (HRV) biomarkers in the identification of anger in adults","fulltext":[{"header":"Introduction","content":"\u003cp\u003eHumans view the world through a perceptual and emotional filter that is formed by a wide and diverse range of personality-oriented thoughts and behaviors (1, 2). Contemporary researchers consider emotions as the foundation of human growth and relationships(3, 4). Since emotions are social in nature, they are a basic means of communication for humans. Unfortunately, there are people who don't know what their emotions are (they don't know their emotions), let alone how to express them(5, 6). Emotion is often defined as a complex feeling that results in physical and psychological changes that affect thought and behavior(7, 8).\u003c/p\u003e \u003cp\u003eDespite the fact that psychologists have paid much attention to emotions in recent years, few clinical and objective recommendations have been provided to effectively deal with specific emotions(9). In fact, if we can accurately identify emotions and measure them objectively, we can greatly help reduce emotional problems and increase people's mental health. Therefore, the methods of identifying and evaluating emotions presented in the research literature can be classified into two main groups according to the basic techniques used in the detection of emotions: based on self-evaluation of emotions by completing different questionnaires(10, 11). Machine evaluation techniques based on measuring different parameters of the human body (12, 13). It is difficult to assess emotions in clinical settings, which must be obtained by periodic self-reports by patients. Self-reporting always depends on people's willingness to tell and is subject to intra-subject variation in reporting due to other contextual factors such as environment, people, situation, etc(14).\u003c/p\u003e \u003cp\u003eHowever, one of the most distinctive of these ways is the monitoring of human physiological changes. There are signals that the human body shows involuntarily, such as heart rate variability, skin/muscle tension, facial expressions, pupil dilation, and micro-movements. Most of them have a bio-electric shock which in turn can be recorded by sensors(15). Using biofeedback, these signals can identify and regulate emotion, which has been widely investigated in many studies(16, 17). Considering the large number of available biological signals, one of the problems is choosing the most appropriate biological signals, which are used as a method to convert signals into emotions(18). Therefore, it is very important to use suitable sensors to measure biosignals, and high precision is required to obtain good reliability(19, 20).\u003c/p\u003e \u003cp\u003eFor automatic detection and evaluation, most emotion evaluation studies focus on other classifications (21, 22) which includes the dimensions of emotions, in most cases capacity (activation-negative/positive) and arousal (high/low) (23\u0026ndash;25) And it analyzes only the basic emotions such as anger, fear, sadness, happiness, surprise, disgust and contempt, which can be easily defined and provides an assessment of their intensity. Among the different emotions that we experience, anger has the most negative effect on human health (26\u0026ndash;28) Therefore, this issue may become a complicated issue for the individual, in which case, the need for medical help to deal with it is necessary(28, 29) On the other hand, continuous expression of anger and anger and inability to control it may also lead to failure in social relations and interpersonal interactions(26, 29) Accordingly, the most important benefit of being aware of and paying attention to angry feelings is the opportunity we get to regulate or inhibit our reactions, reassess the situation, and plan actions that are most likely to remove the source of the anger(30\u0026ndash;32)\u003c/p\u003e \u003cp\u003eThe ability to recognize emotions early can help us deal with people in various situations and manage our emotional responses to their feelings. Furthermore, the work we do to develop impulse awareness can be beneficial for what almost all of us can achieve\u0026mdash;being aware of emotional behavior, or recognizing our emotional state as soon as it begins to express itself in words or actions(27, 33) If you can become aware that an emotion has started to guide your behavior, you can consciously check whether your emotional reaction is appropriate for the situation you are in, and if so, whether it has the right intensity and express yourself in the most constructive way. does or not(30, 34)\u003c/p\u003e \u003cp\u003eRecently, authors extracted features from facial expressions and EEG of 27 subjects. They combined these methods and obtained the highest accuracies of 66.28% and 63.22% for excitation using decision-level fusion.(35) Also, other authors(36) collected RSP, EMG and SC from 20 subjects. They obtained the highest detection rate of 80% for arousal and 76.67% for capacity by combining these three methods. When the 4 categories were arbitrarily adjusted to account for the arousal and valence dimensions, the highest accuracy of 50% was achieved with single-modality RSP or EMG rather than modality fusion. In the well-documented literature, \"individual differences\" have been raised as a matter of widespread concern. Humans may show different emotions for the same emotion and have different physiological patterns when exposed to an emotional stimulus. This issue was first raised by Pickard et al. The main motive of using physiological signals as opposed to subjective personal reports in the detection of emotions was to discover the internal connection of signal patterns with human emotional states and finally to accurately detect emotions (37, 38).\u003c/p\u003e \u003cp\u003eValenza et al. (2012) suggested that heart rate variability (HRV) can be an objective tool to assess emotional responses (2, 39, 40). Also, Lin et al. (2009) reported the relationship between the subject's emotional state and HRV (4). Although HRV is now used sporadically in experiments designed to assess human emotional states, no research has yet confirmed its validity as a tool for assessing human emotion(6, 8)\u003c/p\u003e \u003cp\u003eIn the present study, the goal of the researchers is to be able to identify anger using the heart rate sensor - BVP. This research is the first phase of a research based on the design of an emotional smart wristband that is able to identify emotions in real time and give feedback to the person so that he is aware of his emotional state and type of emotion and then he can deal with the situation appropriately. Among the strategies of emotion regulation, show that it leads to better and more adaptation. Therefore, the obtained diagnostic accuracy helps us to know if heart rate can be the right tool to evaluate and detect emotions in real time?\u003c/p\u003e"},{"header":"Method","content":"\u003cp\u003eThe study is descriptive (exploratory) and quantitative analysis. The current research population includes all adults (age range 20\u0026ndash;45 years) living in Tehran. The statistical sample of the current research, which was selected using the available sampling method based on the entry and exit criteria, was 100 people who, after the initial review of the data obtained from their heart rate sensor and preparation for entering the analysis stage, due to insufficient recording time and Lack of accurate recording of heart rate eliminated 24 of them and the remaining sample in the present study was 76. The entry criteria include agreeing to participate in the project and use the BVP sensor, age range of 20\u0026ndash;45 years, living in Tehran, not suffering from heart diseases, blood pressure and blood phobia. Exclusion criteria include discontinuing participation in the research or developing an illness that restricts the person from participating in the research. In the first stage, among the 20 films approved in scientific articles, 3 films that induced the emotion of anger were selected. In fact, 5-minute pieces of films were chosen, in which moments of anger could be clearly perceived. In the next step, volunteers were invited to participate in the research to perform the test steps. The experiment was carried out in an emotion induction phase by showing a film using anger content on the TV screen of Parand Clinic's testing room. At this stage, the heart rate of each subject was recorded by the BVP sensor for 6 minutes. At the task completion stage, fragments of all three films were shown and immediately after the completion of the film, the Panas positive and negative emotions scale prepared online was answered by the subject about the quantity and quality of the emotions he perceived. In fact, following the self-reports of the first 10 subjects using a single subject method, the movie My Bodyguard was finally selected for the first phase of the research due to the better induction of anger. In the following, 100 volunteers also participated in the emotional induction phase, and the biographic data of 76 of them were confirmed for analysis.\u003c/p\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eInstrument\u003c/h2\u003e \u003cp\u003ePanas Positive and Negative Emotions Scale: To measure concurrent validity, the Panas Positive and Negative Emotions Scale prepared and presented by Watson, Clark and Telgen in 1988 will be used. This scale measures 20 items (10 positive feelings and 10 negative feelings) in the majority of words and is generally evaluated on a five-point Likert scale (from strongly agree to strongly disagree). Cronbach's alpha coefficient for this scale was reported as 0.85 in the research of Sohrabi and Hosseini (1381) and Abolghasemi (1383) obtained the internal correlation coefficient of the components in the whole scale between 0.74 and 0.94, all of which were at the level It was significant less than 0.01 and indicates the validity of this structure. Also, in the research of Shiroudi and Ghorbani (2013), the results of a factor analysis for the standardization of PANAS positive and negative emotional scale, which was carried out on 420 students, showed that the reliability coefficient of the tool, which was estimated based on the general formula of Cronbach's alpha, was 0.87. Therefore, this tool can be used in Iranian society (20)\u003c/p\u003e \u003cp\u003eBVP sensor: BVP sensor and Bioline biofeedback device (manufactured by Madinatab, Iran) were used to record heart rate.\u003c/p\u003e \u003cp\u003eHRV is an emotional state assessment technique based on the measurement of heart rate variability, which means beat-to-beat changes over time during a given period of sinus rhythm. Unlike average heart rate variance, which is expressed over a 60-second period, HRV analysis examines the fine-grained time variance within each heart rate cycle and its regularity (22). The change in heart rate is regulated by the synergistic action of the two branches of the autonomic nervous system, i.e. the sympathetic and parasympathetic nervous systems. The heart rate reflects the net effect of the parasympathetic nerves, which slow the heart rate, and the sympathetic nerves, which speed it up. These changes are influenced by emotions, stress and physical exercises. In addition, HRV depends on age and gender, and additional factors include physical and psychological stress, smoking, alcohol, coffee, overweight, and blood pressure, as well as glucose levels, infectious agents, and depression (25)\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eData analysis\u003c/h2\u003e \u003cp\u003eBy implementing the scales, collecting data and recording them in the computer, it became possible to analyze the data. All analyzes related to descriptive statistics, t table of two independent groups were performed by SPSS software. In addition, ROC analysis and sensitivity analysis were performed by Medcalc software.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eDescriptive statics\u003c/p\u003e \u003cp\u003eThe results related to mean, standard deviation, skewness and stretching of five scales HR, RR, LF, HF, LFtoHF-TASK are shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. As can be seen, the values of skewness and elongation of HR, skewness in RR and skewness and elongation in LFtoHF scale are less than (16), therefore, citing Tabachinek and Fidel (1996), it can be concluded that the distribution of scores of the three scales is relatively normal. and they have no problem in terms of curvature and tension.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\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\u003eThe results of descriptive statistics for five scales HR, RR, LF, HF, LftoHF\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eScales\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSD\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSkeweness\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eKurtosis\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e81.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.75\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e754.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e108.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-1.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e7.15\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\u003e1592.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3017.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e17.24\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\u003e3154.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5651.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e6.63\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLFTOHF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.88\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\u003eThe results of the t test are shown in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. Independent groups t-test was used to examine the difference between two groups of high anger and low anger in each scale of HR, RR, LF, HF, LFtoHF.\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\u003eThe results of t test (t) of independent groups\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\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 \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=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eخطای استاندارد برآورد تفاوت\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eتفاوت میانگین ها\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eسطح معنی داری\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003et\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003edf\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eمقیاس ها\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e17/2\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e57/5\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e012/0\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e56/2\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e74\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eHR\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e83/24\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e40/74-\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e004/0\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e99/2-\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e74\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eRR\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e45/725\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e35/984-\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e17/0\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e35/1-\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e74\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eLF\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e14/1357\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e19/1935-\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e15/0\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e42/1-\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e74\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eHF\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e13/0\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e17/0\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e19/0\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e29/1\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e74\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eLFtoHF\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\u003eAs can be seen, there is a significant difference between the scores of the two groups in the HR and RR scales. However, no significant difference was observed in the other three scales. The resulting t-statistic about the HR scale was statistically significant (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 and t = (76) 2.56). Therefore, there is a significant difference between the two groups of high anger and low anger in this scale. The resulting t-statistic about the RR scale was statistically significant (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 and t = -2.99 (76)). Therefore, there is a significant difference between the two groups of high anger and low anger in this scale.\u003c/p\u003e \u003cp\u003ed) Examining the diagnostic accuracy of the scales\u003c/p\u003e \u003cp\u003eROC analysis and sensitivity analysis were used to investigate the three scales HR, RR, LF, HF, and LFtoHF. The results of ROC analysis are presented in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. As it is clear in the table, the RR scale has the highest value of cleanness index ( ) compared to other scales (=\u0026thinsp;0.71) and after that the HR scale has the highest value of cleanness index (=\u0026thinsp;0.69). But the RR scale has a significantly better performance than the other scale, namely HR (Z\u0026thinsp;=\u0026thinsp;0.01 and Z\u0026thinsp;=\u0026thinsp;0.06). This neat index shows that the RR scale is able to correctly classify people into two groups of people with high aggression and low aggression in 71% of cases. Although the HR scale did not obtain a higher purity index (0.69), it has a better performance than other scales (Z\u0026thinsp;=\u0026thinsp;0.06 and 0.01). The rest of the scales i.e. HF, LF, LFtoHF do not have good performance and clinical value.\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\u003eThe results of ROC analysis for two groups of people with high and low aggression\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eScales\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eA\u003csub\u003eZ\u003c/sub\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSE\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCI\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.60\u0026ndash;0.81\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.57\u0026ndash;0.79\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\u003e0.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.47\u0026ndash;0.70\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\u003e0.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.42\u0026ndash;0.66\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLFtoHF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.43\u0026ndash;0.66\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\u003eAs can be seen in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, the level under the curve of the RR scale is 71%, as mentioned above. The only scale that can identify the emotion of anger in people with a probability of 71% and with a confidence interval (0.81\u0026thinsp;\u0026minus;\u0026thinsp;0.60) (P\u0026thinsp;=\u0026thinsp;0.001). In the following chart, this clean index is shown in more detail and in comparison with other scales.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFor this graph, we chose the categorized variable of anger and all the heart rate variables including HR-TASK, RR-TASK, LF-TASK, HF-TASK, LFtoHF-TASK and rock analysis showed that RR-TASK The rest of the variables are more significant.Therefore, we check the RR scale separately in the chart below, and its values are also reported.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eAs seen in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, the area under the ROC curve for the RR scale is 71% (P\u0026thinsp;=\u0026thinsp;0.001). This means that the heart rate RR scale can detect anger in highly aggressive individuals with a probability of 71%. The sensitivity and specificity are also explained in the following tables.\u003c/p\u003e \u003cp\u003eIn order to determine the optimal cut score for five scales RR, HR, HF, LF, LFtoHF, sensitivity analysis was used. In sensitivity analysis, several characteristics are considered, which include sensitivity, specificity, positive likelihood ratio and negative likelihood ratio. Of course, sometimes, instead of positive probability ratio and negative probability ratio, two indicators of false positive rate and false negative rate are considered. A suitable cut-off score is a test score that can optimize the value of the mentioned 4 characteristics. Among the 4 mentioned characteristics, sensitivity is very important. The optimal cut-off score is a score that, while keeping the sensitivity and specificity values at their maximum value, keeps the negative probability ratio at its minimum value. Therefore, in a wide range of cut-off points (examination scores), only one score represents the desired cut-off score that can maintain the mentioned state. It is also important to mention this point that regardless of the cut-off score provided, this cut-off score is effective when the scale has a good purity index .\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\u003eArea under the ROC curve for the RR scale\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\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 \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eArea under the ROC curve (AUC)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSE\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eZ\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003esig\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.60\u0026ndash;0.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.001\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\u003eجدول 5: شاخص Youden برای مقیاس RR\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Taba\" border=\"1\"\u003e \u003ccolgroup cols=\"4\"\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 \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYouden index\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCut point\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSensivity score\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCharacteristic score\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e66/690\u0026lt;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.45\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\u003eAs stated before, in the current research, only RR has a favorable and relatively good cleanliness index. Therefore, according to the total of two ROC and sensitivity analyses, it can be concluded that if a person scores higher than 690.66 in the RR scale, there is a 71% probability that he has anger and aggressive behaviors. These results are favorable compared to previous similar studies and this shows that the use of a heart rate sensor can be a suitable and reliable tool to detect and identify the emotion of anger.\u003c/p\u003e \u003cp\u003eIn order to determine the optimal cutoff, we are faced with an index called the Youden index. Based on this index, our optimal and desirable cut-off point is the one with the highest sum of sensitivity and specificity. The accuracy in the above results shows that the number 690/66 with sensitivity equal to 88.00% can be considered as the best cut point.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eUndoubtedly, the existence of accurate measurement tools can be an important step in the direction of early diagnosis of problems related to emotions and emotional dysregulation. In the present study, we tried to identify the emotion of anger in people using one of the biomarkers called heart rate variability (HRV). In this way, the diagnostic accuracy of five subscales related to heart rate, i.e. HR, RR, HF, LF and LFtoHF, was investigated among subjects with high anger and low anger.\u003c/p\u003e \u003cp\u003eDescriptive calculations in order to investigate the distribution of people's scores in these five scales showed that all five scales have relatively normal distribution and do not have any problems in terms of skewness and stretching. Preliminary studies on the difference between the mean scores of two groups of people with high and low anger in five scales HR, RR, HF, LF and LFtoHF showed that the difference between the mean scores of the two groups in the HR scale (at a significant level of less than 0.05) and RR (at a significance level of less than 0.001) is statistically significant. In both scales, the average scores of people with high anger were higher than the average of people with low anger. Determining the optimal cut-off score for these scales based on the observed differences, in addition to being associated with many errors, does not have strong statistical support; Therefore, ROC and sensitivity analysis were used to determine the optimal cut-off score and also to examine the diagnostic accuracy of these scales.\u003c/p\u003e \u003cp\u003eAmong the five HR, RR, HF, LF and LFtoHF scales, the RR scale had the highest purity index (=\u0026thinsp;0.71). This scale performed significantly better than other HR, HF, LF and LFtoHF scales. Since in clinical works, the purity index ( ) above 0.70 is usually considered as a relatively good and desirable index, therefore, in the present study, only the purity index of the RR scale is clinically acceptable. The purity index value of HR scale (=\u0026thinsp;0.69) is also less than 0.71, clinically it is not a desirable index, but it performs better than other scales. Therefore, it can be concluded that based on the ROC analysis, the RR scale is able to correctly classify people into two groups of people with high anger and people with low anger in 71% of cases, and HR scale in 69% of cases\u003c/p\u003e \u003cp\u003eThese findings are in line with the research of Chang et al. (2021) who distinguished happiness and sadness with the help of artificial intelligence using HRV. The results of their research showed that these emotions can be identified by HRV with a sensitivity of 70.7% and a specificity of 58.4%. Also, with studies such as Chen et al. (2017) who in China obtained the average accuracy of emotional intelligent device detection of 77.57% in detecting four emotions with the best accuracy of 86.67% for positive emotion and excited emotion detection (37), Haag et al. 21) In a research in Germany, researchers reached a diagnostic accuracy of 89.90% in the capacity dimension and 96.60% in the arousal dimension, and Wagner et al. They reached 92.05% accuracy to detect four emotions, it is consistent. Recently, Huang et al. (37) extracted features from facial expressions and EEG of 27 subjects. They combined these methods and obtained the highest accuracy of 66.28% for capacity.\u003c/p\u003e \u003cp\u003eFinally, this research showed that HRV can be used as an objective method to identify emotions. Also, we emphasize that while the use of biosignals is a valuable tool for emotion recognition, there is currently a question as to whether it alone is sufficient. It seems that combining different sources on top of biosensor signals, such as video analysis, motion detection or emotion recognition from speech, is a necessary step to avoid the limitations of single-modality systems.\u003c/p\u003e \u003cp\u003eIn the present study, due to the small size of the sample, the results cannot be attributed to all people with certainty. Therefore, future researches should clarify the validity and reliability of these findings (with a larger sample size and in other groups). In general, the current research can be considered as a preliminary research in order to measure the diagnostic accuracy of heart rate measurement scales.\u003c/p\u003e "},{"header":"Declarations","content":"\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eB. D created the idea and devised the research plan. Then, he handled the correspondence for the official permission in order to conduct the study. M. N and Z. D proceeded with the data collection. M. N. and B. D wrote the entire article and performed the statistical analyses. M. M reviewed the drafts and corrected the mistakes. H. F supervised the process from the beginning to the end. B. D submitted the article. He is the corresponding author.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eDemasio AR. Descartes\u0026rsquo; error: emotion, reason and the human brain. New York: Putnam. 1994.\u003c/li\u003e\n\u003cli\u003eValenza G, Lanata A, Scilingo EP. The role of nonlinear dynamics in affective valence and arousal recognition. IEEE transactions on affective computing. 2011;3(2):237\u0026thinsp;\u0026minus;\u0026thinsp;49.\u003c/li\u003e\n\u003cli\u003eRahmani E, Rahmanian M, Mansouri K, Mokhayeri Y, Jamalpour Y, Hassanvandi S. Are There any Possible Side Effects of Neurofeedback? 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Computer Vision and Image Understanding. 2016;147:114\u0026thinsp;\u0026minus;\u0026thinsp;24.\u003c/li\u003e\n\u003cli\u003eZhang L, Rukavina S, Gruss S, Traue HC, Hazer D, editors. Classification analysis for the emotion recognition from psychobiological data. ISCT; 2015.\u003c/li\u003e\n\u003cli\u003eChen J, Hu B, Wang Y, Moore P, Dai Y, Feng L, et al. Subject-independent emotion recognition based on physiological signals: a three-stage decision method. BMC medical informatics and decision making. 2017;17:45\u0026ndash;57.\u003c/li\u003e\n\u003cli\u003eShahyad S, Rahmani E, Nikdanesh M, Ashoori A, Azadi S, Hassanvandi S. Effectiveness of Neurofeedback on Psychological Stress, Salivary Cortisol and \u0026alpha;-amylase Level in Students: A Randomized and Parallel-Group Clinical Trial. Iranian Journal of Psychiatry and Behavioral Sciences. 2024(In Press).\u003c/li\u003e\n\u003cli\u003eBalootbangan AA, Mahvelaty A, Zamani Z, Abdpoor F, Hassanvandi S. 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Archives of Iranian Medicine. 2022;25(5):285\u0026ndash;393.\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":"heart rate variability, biomarkers, anger, emotion dysregulation","lastPublishedDoi":"10.21203/rs.3.rs-3965051/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3965051/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eUndoubtedly, the existence of accurate measurement tools can be an important step in the direction of early diagnosis of problems related to emotions and emotional dysregulation.\u003c/p\u003e\u003ch2\u003eObjective\u003c/h2\u003e \u003cp\u003eI n the present study, we tried to identify the emotion of anger in people using one of the biomarkers called heart rate variability (HRV). In this way, the diagnostic accuracy of five subscales related to heart rate, i.e. HR, RR, HF, LF and LFtoHF, was investigated among subjects with high anger and low anger.\u003c/p\u003e\u003ch2\u003eMethod\u003c/h2\u003e \u003cp\u003eThe study is descriptive (exploratory) and quantitative analysis. The current research population includes all adults (age range 20\u0026ndash;45 years selected based on research literature) living in Tehran. The statistical sample of the current research, which was selected using the available sampling method based on the entry and exit criteria, was 100 people who, after the initial review of the data obtained from their heart rate sensor and preparation for entering the analysis stage, due to insufficient recording time and Lack of accurate recording of heart rate eliminated 24 of them and the remaining sample in the present study was 76. The instrument of the current research consists of two parts: Panas positive and negative emotions scale and BVP sensor. All analyzes related to descriptive statistics, t table of two independent groups were performed by SPSS software. In addition, ROC analysis and sensitivity analysis were performed by Medcalc software.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003ePreliminary studies on the difference in the mean scores of the two groups of people with high and low anger in five scales HR, RR, HF, LF and LFtoHF showed that the difference in the mean scores of the two groups in the HR scale (at a significant level of less than 0.05) and RR (at a significance level of less than 0.001) is statistically significant. In both scales, the average scores of people with high anger were higher than the average scores of people with low anger. Among the five HR, RR, HF, LF and LFtoHF scales, the RR scale had the highest purity index (=\u0026thinsp;0.71). This scale performed significantly better than other HR, HF, LF and LFtoHF scales.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eThe results of this study can make the use of biological indicators more prominent in identifying positive and negative emotions.\u003c/p\u003e","manuscriptTitle":"The use of heart rate variability (HRV) biomarkers in the identification of anger in adults","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-02-21 20:16:10","doi":"10.21203/rs.3.rs-3965051/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":"24b8457d-2612-4108-88a9-26db9dcb8540","owner":[],"postedDate":"February 21st, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-08-15T18:59:23+00:00","versionOfRecord":[],"versionCreatedAt":"2024-02-21 20:16:10","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3965051","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3965051","identity":"rs-3965051","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}