scholarly journals Pain Monitoring Using Heart Rate Variability and Photoplethysmograph-Derived Parameters by Binary Logistic Regression

2021 ◽  
Author(s):  
D. F. Jhang ◽  
Y. S. Chu ◽  
J. H. Cai ◽  
Y. Y. Tai ◽  
C. C. Chuang
Keyword(s):  
Heart Rate ◽  
Heart Rate Variability ◽  
Logistic Regression ◽  
Rating Scale ◽  
Binary Logistic Regression ◽  
Numeric Rating Scale ◽  
Classification Model ◽  
Generation Process ◽  
Postoperative Intensive Care ◽  
Postoperative Intensive Care Unit

Abstract Purpose To construct a pain classification model using binary logistic regression to calculate pain probability and monitor pain based on heart rate variability (HRV) and photoplethysmography (PPG) parameters. Methods Heat stimulation was used to simulate pain for modeling the pain generation process, and electrocardiography and PPG signals were recorded simultaneously. After signal analysis, statistical analysis was performed using SPSS to determine the parameters that were significant for pain. Thereafter, a pain classification model with HRV and PPG parameters was established using binary logistic regression. Results The sensitivity and specificity of the pain classification model were 60.0% and 72.0%, respectively. When pain occurred, the probability calculated using the pain classification model increased from < 50% to > 50%. When the pain was relieved, the probability decreased to < 50%. The probability of pain was consistent with the numeric rating scale value, which indicated that the model can correctly determine the presence of pain. Conclusion This pain classification model has sufficient robustness and adaptability to be applied to different healthy people for classification and monitoring. This model is helpful in establishing a real-time pain monitoring system to improve pain management for patients in the postoperative intensive care unit and patient-controlled analgesia and provide a reference for doctors regarding medication.

scholarly journals Effect of Virtual Reality on Stress Reduction and Change of Physiological Parameters Including Heart Rate Variability in People With High Stress: An Open Randomized Crossover Trial

2021 ◽  
Vol 12 ◽  
Author(s):  
Hyewon Kim ◽  
Dong Jun Kim ◽  
Seonwoo Kim ◽  
Won Ho Chung ◽  
Kyung-Ah Park ◽  
...  
Keyword(s):  
Heart Rate ◽  
Heart Rate Variability ◽  
Virtual Reality ◽  
Rating Scale ◽  
High Stress ◽  
Mental Healthcare ◽  
Numeric Rating Scale ◽  
Physiological Parameters ◽  
Relaxation Phase ◽  
Significant Difference

Introduction: Although, attempts to apply virtual reality (VR) in mental healthcare are rapidly increasing, it is still unclear whether VR relaxation can reduce stress more than conventional biofeedback.Methods: Participants consisted of 83 healthy adult volunteers with high stress, which was defined as a score of 20 or more on the Perceived Stress Scale-10 (PSS-10). This study used an open, randomized, crossover design with baseline, stress, and relaxation phases. During the stress phase, participants experienced an intentionally generated shaking VR and serial-7 subtraction. For the relaxation phase, participants underwent a randomly assigned relaxation session on day 1 among VR relaxation and biofeedack, and the other type of relaxation session was applied on day 2. We compared the State-Trait Anxiety Inventory-X1 (STAI-X1), STAI-X2, the Numeric Rating Scale (NRS), and physiological parameters including heart rate variability (HRV) indexes in the stress and relaxation phases.Results: A total of 74 participants were included in the analyses. The median age of participants was 39 years, STAI-X1 was 47.27 (SD = 9.92), and NRS was 55.51 (SD = 24.48) at baseline. VR and biofeedback significantly decreased STAI-X1 and NRS from the stress phase to the relaxation phase, while the difference of effect between VR and biofeedback was not significant. However, there was a significant difference in electromyography, LF/HF ratio, LF total, and NN50 between VR relaxation and biofeedback.Conclusion: VR relaxation was effective in reducing subjectively reported stress in individuals with high stress.

scholarly journals Stress Markers for Mental States and Biotypes of Depression and Anxiety: A Scoping Review and Preliminary Illustrative Analysis

2021 ◽  
Vol 5 ◽  
pp. 247054702110003
Author(s):  
Megan Chesnut ◽  
Sahar Harati ◽  
Pablo Paredes ◽  
Yasser Khan ◽  
Amir Foudeh ◽  
...  
Keyword(s):  
Heart Rate ◽  
Heart Rate Variability ◽  
Skin Conductance ◽  
Neural Circuit ◽  
Current Knowledge ◽  
Mental States ◽  
Classification Model ◽  
Depression And Anxiety ◽  
Stress Markers ◽  
Physiological Markers

Depression and anxiety disrupt daily function and their effects can be long-lasting and devastating, yet there are no established physiological indicators that can be used to predict onset, diagnose, or target treatments. In this review, we conceptualize depression and anxiety as maladaptive responses to repetitive stress. We provide an overview of the role of chronic stress in depression and anxiety and a review of current knowledge on objective stress indicators of depression and anxiety. We focused on cortisol, heart rate variability and skin conductance that have been well studied in depression and anxiety and implicated in clinical emotional states. A targeted PubMed search was undertaken prioritizing meta-analyses that have linked depression and anxiety to cortisol, heart rate variability and skin conductance. Consistent findings include reduced heart rate variability across depression and anxiety, reduced tonic and phasic skin conductance in depression, and elevated cortisol at different times of day and across the day in depression. We then provide a brief overview of neural circuit disruptions that characterize particular types of depression and anxiety. We also include an illustrative analysis using predictive models to determine how stress markers contribute to specific subgroups of symptoms and how neural circuits add meaningfully to this prediction. For this, we implemented a tree-based multi-class classification model with physiological markers of heart rate variability as predictors and four symptom subtypes, including normative mood, as target variables. We achieved 40% accuracy on the validation set. We then added the neural circuit measures into our predictor set to identify the combination of neural circuit dysfunctions and physiological markers that accurately predict each symptom subtype. Achieving 54% accuracy suggested a strong relationship between those neural-physiological predictors and the mental states that characterize each subtype. Further work to elucidate the complex relationships between physiological markers, neural circuit dysfunction and resulting symptoms would advance our understanding of the pathophysiological pathways underlying depression and anxiety.

Autonomie Correlates of Antidepressant Treatment Using Heart-Rate Variability Analysis

1998 ◽  
Vol 43 (2) ◽  
pp. 183-186 ◽  
Author(s):  
Yaariv Khaykin ◽  
Paul Dorian ◽  
Brian Baker ◽  
Colin Shapiro ◽  
Paul Sandor ◽  
...  
Keyword(s):  
Heart Rate ◽  
Heart Rate Variability ◽  
Standard Deviation ◽  
Rating Scale ◽  
Antidepressant Treatment ◽  
Response To Therapy ◽  
Heart Rate Variability Analysis ◽  
Randomized Evaluation ◽  
Temporal Domain ◽  
Percentage Difference

Objective: To assess the 24-hour temporal-domain heart-rate variability correlates of treatment with fluoxetine or doxepinfor depression. Method: A randomized evaluation of fluoxetine and doxepin measured a 50% change in the Hamilton Depression Rating Scale (HDRS) score as a response to therapy and was correlated with measures of standard deviation of the mean of all 5-minute segments of normal electrocardiographic R-R intervals (SDANN), standard deviation of all normal R-R intervals (SDNN), root mean square of successive differences in R-R intervals (r-MSSD), and percentage difference between adjacent normal R-R intervals that are greater than 50 msec (pNN50)from 24-hour electrocardiogram (ECG) tapes. Results: Ten out of 14 patients responded. Response was associated with an increase in SDANN of 17% (P < 0.05). Nonresponse was associated with a 17% decrease in SDANN and a 22% decrease in SDNN (both P < 0.05). No other measures correlated with therapeutic response. No heart-rate variability (HRV) differences between the 2 drug therapies were observed. Conclusion: Twenty-four-hour HRV measures may be useful in assessing response to antidepressant therapy.

scholarly journals Reduction of depressive symptoms during inpatient treatment is not associated with changes in heart rate variability

PLoS ONE ◽  
2021 ◽  
Vol 16 (3) ◽  
pp. e0248686
Author(s):  
Sabrina Neyer ◽  
Michael Witthöft ◽  
Mark Cropley ◽  
Markus Pawelzik ◽  
Ricardo Gregorio Lugo ◽  
...  
Keyword(s):  
Heart Rate ◽  
Heart Rate Variability ◽  
Depressive Symptoms ◽  
Rating Scale ◽  
Psychiatric Treatment ◽  
Self Report ◽  
Future Research ◽  
Symptom Improvement ◽  
Vagal Activity ◽  
Before And After

Vagally mediated heart rate variability (HRV) is a psychophysiological indicator of mental and physical health. Limited research suggests there is reduced vagal activity and resulting lower HRV in patients with Major Depressive Disorder (MDD); however little is actually known about the association between HRV and symptoms of depression and whether the association mirrors symptom improvement following psychotherapy. The aim of this study was to investigate the association between antidepressant therapy, symptom change and HRV in 50 inpatients (68% females; 17–68 years) with a diagnosis of MDD. Severity of depressive symptoms was assessed by self-report (Beck Depression Inventory II) and the Hamilton Rating Scale of Depression. Measures of vagally mediated HRV (root mean square of successive differences and high-frequency) were assessed at multiple measurement points before and after inpatient psychotherapeutic and psychiatric treatment. Results showed an expected negative correlation between HRV and depressive symptoms at intake. Depressive symptoms improved (d = 0.84) without corresponding change in HRV, demonstrating a de-coupling between this psychophysiological indicator and symptom severity. To our knowledge, this study is the first to examine an association between HRV and depressive symptoms before and after psychotherapy. The observed de-coupling of depression and HRV, and its methodological implications for future research are discussed.

scholarly journals Association of Resting Heart Rate and Heart Rate Variability With Proximal Suicidal Risk in Patients With Diverse Psychiatric Diagnoses

2021 ◽  
Vol 12 ◽  
Author(s):  
Dongbin Lee ◽  
Ji Hyun Baek ◽  
Yun Ji Cho ◽  
Kyung Sue Hong
Keyword(s):  
Heart Rate ◽  
Heart Rate Variability ◽  
Logistic Regression ◽  
Linear Regression ◽  
Risk Prediction ◽  
Suicide Risk ◽  
Psychiatric Diagnoses ◽  
Resting Heart Rate ◽  
Regression Analyses ◽  
Suicidal Risk

Objectively measurable biomarkers have not been applied for suicide risk prediction. Resting heart rate (HR) and heart rate variability (HRV) showed potential as trans-diagnostic markers associated with suicide. This study aimed to investigate the associations of resting HR and HRV on proximal suicide risk in patients with diverse psychiatric diagnoses. This chart review study used the medical records of psychiatric patients who visited the outpatient clinic at an academic tertiary hospital. A total of 1,461 patients with diverse psychiatric diagnoses was included in the analysis. Proximal suicide risk was measured using the Mini-International Neuropsychiatric Interview (MINI) suicidal score. Linear regression analyses with the MINI suicidal score as a dependent variable and binary logistic regression analyses with moderate-to-high suicide risk (MINI suicidal risk score ≥6) as a dependent variable were conducted to explore the effects of resting HR and HRV parameters on acute suicide risk after adjusting for age, sex, presence of major depressive disorder (MDD) and bipolar disorder (BD), severity of depression and anxiety severity. We found that 55 (34.6%) patients in the MDD group, 40 (41.7%) in the BD group and 36 (3.9%) in the others group reported moderate-to-high suicide risk. Linear regression analysis revealed that both resting HR and root-mean-square of successive difference (RMSSD) had significant associations with the MINI suicidal score (P = 0.037 with HR, P = 0.003 with RMSSD). In logistic regression, only RMSSD showed a significant association with moderate-to-high suicide risk (P = 0.098 with HR, P = 0.019 with RMSSD), which remained significant in subgroup analysis with patients who reported any suicide-related symptom (MINI suicidal score &gt;0; n = 472; P = 0.017 with HR, P = 0.012 with RMSSD). Our study findings suggest the potential for resting HR and RMSSD as biomarkers for proximal suicide risk prediction. Further research with longitudinal evaluation is needed to confirm our study findings.

scholarly journals Family support is not a risk factor of negative self-esteem in HIV/AIDS women

2016 ◽  
Vol 34 (1) ◽  
pp. 61
Author(s):  
Jean Valeria ◽  
Surilena Surilena ◽  
Yanto Budiman ◽  
Samsuridjal Djauzi ◽  
Haridana Indah
Keyword(s):  
Logistic Regression ◽  
Protective Effect ◽  
Family Support ◽  
Rating Scale ◽  
Binary Logistic Regression ◽  
Self Esteem ◽  
Psychosocial Burden ◽  
Poor Family ◽  
Good Family ◽  
Hiv Aids

BACKGROUND Women with HIV/AIDS (WLWHA) have a complex psychosocial burden and a tendency to negative self-esteem, possibly resulting in mental and emotional problems. They need family support to deal with the HIV/AIDS infection and its psychosocial burden. The purpose of this study was to determine chacteristics of family support, self-esteem, and depression of WLWHA and the relationship between family support and self-esteem and depression. METHOD This was a cross-sectional study of 99 WLWHA infected through their husbands/partners, with no history of drug abuse. The data was taken by a consecutive sampling of two proportions test at Dharmais Cancer Hospital from November 2013 – January 2014. The instruments comprised a demographic questionnaire, the Rosenberg Self-Esteem questionnaire, the Hamilton Depression Rating Scale (HDRS), and a family support questionnaire. The data was analyzed by binary logistic regression. RESULTS There were 99 respondents with mean age of 36 years, of whom 44.4% were high school graduates, 54.5% unemployed, and 91.9% had HIV/ AIDS for more than a year. Binary logistic regression analysis showed no significant relationship between family support and self-esteem (p=0.700) and depression (p=0.396). Good family support has a protective effect of 1.3 times (OR=0.772; 95%CI: 0.138-3.770) towards increasing self-esteem, whereas poor family support increases the risk of depression 1.5 times (OR=1.477; 95%CI: 0.598-3.645) in WLWHA infected with HIV/AIDS from their husband/partner. CONCLUSIONS Good family support tend to have a protective effect towards increasing self-esteem, whereas poor family support increases the risk of depression in WLWHA infected with HIV/AIDS from their husband/partner.

scholarly journals Correlation between Palpitations below the Heart in Traditional Chinese Medicine and Autonomic Nerve Function Based on Heart Rate Variability: A Case-Control Study

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Chengcheng Song ◽  
Kelong Chen ◽  
Ziqian Wu ◽  
Wei Liu ◽  
Ling Chen ◽  
...  
Keyword(s):  
Heart Rate ◽  
Heart Rate Variability ◽  
Logistic Regression ◽  
Risk Factor ◽  
Chinese Medicine ◽  
Traditional Chinese Medicine ◽  
Autonomic Nerve ◽  
Nerve Function ◽  
Spearman Correlation ◽  
Autonomic Nerve Function

Objective. To explore the autonomic nerve rhythm and the correlation between palpitations below the heart (PBTH) and autonomic nerve function in patients with PBTH based on heart rate variability (HRV). Methods. The outpatients or ward patients of Wenzhou Hospital of Traditional Chinese Medicine were collected and divided into two groups: the PBTH group and the normal group. The HRV of each group was detected. Single-factor statistical methods, Spearman correlation analysis, and logistic regression were used to describe and analyze the rhythm and characteristics of autonomic nerves in patients with PBTH and the correlation between PBTH and autonomic nerve function. Results. (1) In the comparison of HRV in different time periods in the same group, the SDNN, RMSSD, pNN50, TP, and HF in the PBTH group at night were significantly higher than those in the daytime ( P < 0.01 ), while the LF/HF ratio was significantly lower than that in the daytime ( P < 0.01 ). (2) In the comparison of HRV between the two groups in the same time period, the RMSSD and pNN50 of the PBTH group during the daytime period were significantly higher than those of the normal control group ( P < 0.05 ), and the LF/HF was significantly lower than that of the normal group ( P < 0.05 ). (3) In the Spearman correlation analysis, PBTH was significantly correlated with RMSSD, pNN50, and LF/HF ratio in the daytime period, with correlation coefficients of 0.424, 0.462, and −0.524, respectively ( P < 0.05 ). (4) Logistic regression analysis showed that the decrease of LF/HF ratio during the daytime period was an independent risk factor for PBTH in TCM (OR = 0.474, 95% CI: 0.230–0.977, P < 0.05 ). Conclusions. The changes in parasympathetic nerve function in patients with PBTH have a circadian rhythm, which is characterized by increased activity during the nighttime. At the same time, the autonomic nerve activity of people with PBTH during the daytime is unbalanced, and the decrease of LF/HF ratio during the day is an independent high risk factor for PBTH.

Impaired heart rate variability triangular index to identify clinically silent strokes in patients with atrial fibrillation

2020 ◽  
Vol 41 (Supplement_2) ◽  
Author(s):  
P Haemmerle ◽  
C Eick ◽  
A Bauer ◽  
K.D Rizas ◽  
M Coslovsky ◽  
...  
Keyword(s):  
Atrial Fibrillation ◽  
Heart Rate ◽  
Heart Rate Variability ◽  
Transient Ischemic Attack ◽  
Brain Mri ◽  
Binary Logistic Regression ◽  
Funding Source ◽  
Logistic Regression Models ◽  
History Of ◽  
Ischemic Attack

Abstract Introduction The identification of clinically silent strokes in patients with atrial fibrillation (AF) is of high clinical relevance as they have been linked to cognitive impairment. Overt strokes have been associated with disturbances of the autonomic nervous system. Purpose We therefore hypothesize that impaired heart rate variability (HRV) can identify AF patients with clinically silent strokes. Methods We enrolled 1358 patients with AF without a history of stroke or transient ischemic attack from the multicenter SWISS-AF cohort study who were in sinus rhythm (SR-group, n=816) or AF (AF-group, n=542) on a 5 minute resting ECG recording. HRV triangular index (HRVI), the standard deviation of normal-to-normal intervals (SDNN) and the mean heart rate (MHR) were calculated. Brain MRI was performed at baseline to assess the presence of large non-cortical or cortical infarcts, which were considered silent strokes without history of stroke or transient ischemic attack. We constructed binary logistic regression models to analyze the association between HRV parameters and silent strokes. Results At baseline, silent strokes were detected in 10.5% in the SR group and 19.9% in the AF group. In the SR-group, HRVI &lt;15 was the only parameter independently associated with the presence of silent strokes (odds ratio (OR) 1.69; 95% confidence interval (CI): 1.04–2.72; p=0.033) after adjustment for various clinical covariates (age, sex, systolic blood pressure, history of hypertension, history of diabetes, history of heart failure, prior myocardial infarction, prior major bleeding, intake of oral anticoagulation, antiarrhythmics or betablockers). Similarly, in the AF-group, HRVI&lt;15 was independently associated with the presence of silent strokes (OR 1.65, 95% CI: 1.05–2.57; p=0.028). SDNN&lt;70ms and MHR&lt;80 were not associated with silent strokes, neither in the SR group, nor in the AF group (Figure). Conclusions Reduced HRVI is independently associated with the presence of clinically silent strokes in an AF population, both when assessed during SR and during AF. Our data suggest that a short-term measurement of HRV in routine ECG recordings might contribute to identifying AF patients with clinically silent strokes. Funding Acknowledgement Type of funding source: Foundation. Main funding source(s): Swiss National Science Foundation

Development of a Classification Model for Driver's Drowsiness and Waking Status Using Heart Rate Variability and Respiratory Features

2016 ◽  
Vol 35 (5) ◽  
pp. 371-381 ◽  
Author(s):  
Sungho Kim ◽  
Booyong Choi ◽  
Taehwan Cho ◽  
Yongkyun Lee ◽  
Hyojin Koo ◽  
...  
Keyword(s):  
Heart Rate ◽  
Heart Rate Variability ◽  
Classification Model

scholarly journals Identification of Video Game Addiction Using Heart-Rate Variability Parameters

Sensors ◽  
2021 ◽  
Vol 21 (14) ◽  
pp. 4683
Author(s):  
Jung-Yong Kim ◽  
Hea-Sol Kim ◽  
Dong-Joon Kim ◽  
Sung-Kyun Im ◽  
Mi-Sook Kim
Keyword(s):  
Heart Rate ◽  
Heart Rate Variability ◽  
Logistic Regression ◽  
Video Game ◽  
Diagnostic Method ◽  
Factor Analyses ◽  
Effective Parameters ◽  
Game Addiction ◽  
League Of Legends ◽  
Logistic Regression Equation

The purpose of this study is to determine heart rate variability (HRV) parameters that can quantitatively characterize game addiction by using electrocardiograms (ECGs). 23 subjects were classified into two groups prior to the experiment, 11 game-addicted subjects, and 12 non-addicted subjects, using questionnaires (CIUS and IAT). Various HRV parameters were tested to identify the addicted subject. The subjects played the League of Legends game for 30–40 min. The experimenter measured ECG during the game at various window sizes and specific events. Moreover, correlation and factor analyses were used to find the most effective parameters. A logistic regression equation was formed to calculate the accuracy in diagnosing addicted and non-addicted subjects. The most accurate set of parameters was found to be pNNI20, RMSSD, and LF in the 30 s after the “being killed” event. The logistic regression analysis provided an accuracy of 69.3% to 70.3%. AUC values in this study ranged from 0.654 to 0.677. This study can be noted as an exploratory step in the quantification of game addiction based on the stress response that could be used as an objective diagnostic method in the future.

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