Heart rate variability, postural sway and electrodermal activity in competitive golf putting

Recently, there has been an increase in the production of devices to monitor mental health and stress as means for expediting detection, and subsequent management of these conditions. The objective of this review is to identify and critically appraise the most recent smart devices and wearable technologies used to identify depression, anxiety, and stress, and the physiological process(es) linked to their detection. The MEDLINE, CINAHL, Cochrane Central, and PsycINFO databases were used to identify studies which utilised smart devices and wearable technologies to detect or monitor anxiety, depression, or stress. The included articles that assessed stress and anxiety unanimously used heart rate variability (HRV) parameters for detection of anxiety and stress, with the latter better detected by HRV and electroencephalogram (EGG) together. Electrodermal activity was used in recent studies, with high accuracy for stress detection; however, with questionable reliability. Depression was found to be largely detected using specific EEG signatures; however, devices detecting depression using EEG are not currently available on the market. This systematic review highlights that average heart rate used by many commercially available smart devices is not as accurate in the detection of stress and anxiety compared with heart rate variability, electrodermal activity, and possibly respiratory rate.

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Trends in Heart-Rate Variability Signal Analysis

Frontiers in Digital Health ◽

10.3389/fdgth.2021.639444 ◽

2021 ◽

Vol 3 ◽

Author(s):

Syem Ishaque ◽

Naimul Khan ◽

Sri Krishnan

Keyword(s):

Machine Learning ◽

Heart Rate ◽

Heart Rate Variability ◽

Electrodermal Activity ◽

Well Being ◽

Good Health ◽

Machine Learning Techniques ◽

Video Gaming ◽

Clinical Events ◽

Learning Techniques

Heart rate variability (HRV) is the rate of variability between each heartbeat with respect to time. It is used to analyse the Autonomic Nervous System (ANS), a control system used to modulate the body's unconscious action such as cardiac function, respiration, digestion, blood pressure, urination, and dilation/constriction of the pupil. This review article presents a summary and analysis of various research works that analyzed HRV associated with morbidity, pain, drowsiness, stress and exercise through signal processing and machine learning methods. The points of emphasis with regards to HRV research as well as the gaps associated with processes which can be improved to enhance the quality of the research have been discussed meticulously. Restricting the physiological signals to Electrocardiogram (ECG), Electrodermal activity (EDA), photoplethysmography (PPG), and respiration (RESP) analysis resulted in 25 articles which examined the cause and effect of increased/reduced HRV. Reduced HRV was generally associated with increased morbidity and stress. High HRV normally indicated good health, and in some instances, it could signify clinical events of interest such as drowsiness. Effective analysis of HRV during ambulatory and motion situations such as exercise, video gaming, and driving could have a significant impact toward improving social well-being. Detection of HRV in motion is far from perfect, situations involving exercise or driving reported accuracy as high as 85% and as low as 59%. HRV detection in motion can be improved further by harnessing the advancements in machine learning techniques.

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Quantitative assessment of the relationship between behavioral and autonomic dynamics during propofol-induced unconsciousness

10.1101/2020.11.03.367607 ◽

2020 ◽

Author(s):

Sandya Subramanian ◽

Patrick L. Purdon ◽

Riccardo Barbieri ◽

Emery N. Brown

Keyword(s):

Heart Rate ◽

Heart Rate Variability ◽

Prediction Models ◽

Electrodermal Activity ◽

Strong Relationship ◽

Pulse Amplitude ◽

Physiological Data ◽

Statistical Tool ◽

Autonomic Changes ◽

Before And After

ABSTRACTDuring general anesthesia, both behavioral and autonomic changes are caused by the administration of anesthetics such as propofol. Propofol produces unconsciousness by creating highly structured oscillations in brain circuits. The anesthetic also has autonomic effects due to its actions as a vasodilator and myocardial depressant. Understanding how autonomic dynamics change in relation to propofol-induced unconsciousness is an important scientific and clinical question since anesthesiologists often infer changes in level of unconsciousness from changes in autonomic dynamics. Therefore, we present a framework combining physiology-based statistical models that have been developed specifically for heart rate variability and electrodermal activity with a robust statistical tool to compare behavioral and multimodal autonomic changes before, during, and after propofol-induced unconsciousness. We tested this framework on physiological data recorded from nine healthy volunteers during computer-controlled administration of propofol. We studied how autonomic dynamics related to behavioral markers of unconsciousness: 1) overall, 2) during the transitions of loss and recovery of consciousness, and 3) before and after anesthesia as a whole. Our results show a strong relationship between behavioral state of consciousness and autonomic dynamics. All of our prediction models showed areas under the curve greater than 0.75 despite the presence of non-monotonic relationships among the variables during the transition periods. Our analysis highlighted the specific roles played by fast versus slow changes, parasympathetic vs sympathetic activity, heart rate variability vs electrodermal activity, and even pulse rate vs pulse amplitude information within electrodermal activity. Further advancement upon this work can quantify the complex and subject-specific relationship between behavioral changes and autonomic dynamics before, during, and after anesthesia. However, this work demonstrates the potential of a multimodal, physiologically-informed, statistical approach to characterize autonomic dynamics.

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Heart Rate Variability, QT Variability, and Electrodermal Activity during Exercise

Medicine & Science in Sports & Exercise ◽

10.1249/mss.0b013e3181b64db1 ◽

2010 ◽

Vol 42 (3) ◽

pp. 443-448 ◽

Cited By ~ 32

Author(s):

SILKE BOETTGER ◽

CHRISTIAN PUTA ◽

VIKRAM K. YERAGANI ◽

LARS DONATH ◽

HANS-JOSEF MÜLLER ◽

...

Keyword(s):

Heart Rate ◽

Heart Rate Variability ◽

Electrodermal Activity ◽

Qt Variability

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Quantitative assessment of the relationship between behavioral and autonomic dynamics during propofol-induced unconsciousness

PLoS ONE ◽

10.1371/journal.pone.0254053 ◽

2021 ◽

Vol 16 (8) ◽

pp. e0254053

Author(s):

Sandya Subramanian ◽

Patrick L. Purdon ◽

Riccardo Barbieri ◽

Emery N. Brown

Keyword(s):

Heart Rate ◽

Heart Rate Variability ◽

Prediction Models ◽

Electrodermal Activity ◽

Strong Relationship ◽

Pulse Amplitude ◽

Physiological Data ◽

Statistical Tool ◽

Autonomic Changes ◽

Before And After

During general anesthesia, both behavioral and autonomic changes are caused by the administration of anesthetics such as propofol. Propofol produces unconsciousness by creating highly structured oscillations in brain circuits. The anesthetic also has autonomic effects due to its actions as a vasodilator and myocardial depressant. Understanding how autonomic dynamics change in relation to propofol-induced unconsciousness is an important scientific and clinical question since anesthesiologists often infer changes in level of unconsciousness from changes in autonomic dynamics. Therefore, we present a framework combining physiology-based statistical models that have been developed specifically for heart rate variability and electrodermal activity with a robust statistical tool to compare behavioral and multimodal autonomic changes before, during, and after propofol-induced unconsciousness. We tested this framework on physiological data recorded from nine healthy volunteers during computer-controlled administration of propofol. We studied how autonomic dynamics related to behavioral markers of unconsciousness: 1) overall, 2) during the transitions of loss and recovery of consciousness, and 3) before and after anesthesia as a whole. Our results show a strong relationship between behavioral state of consciousness and autonomic dynamics. All of our prediction models showed areas under the curve greater than 0.75 despite the presence of non-monotonic relationships among the variables during the transition periods. Our analysis highlighted the specific roles played by fast versus slow changes, parasympathetic vs sympathetic activity, heart rate variability vs electrodermal activity, and even pulse rate vs pulse amplitude information within electrodermal activity. Further advancement upon this work can quantify the complex and subject-specific relationship between behavioral changes and autonomic dynamics before, during, and after anesthesia. However, this work demonstrates the potential of a multimodal, physiologically-informed, statistical approach to characterize autonomic dynamics.

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Heart Rate Variability and Electrodermal Activity in Mental Stress Aloud: Predicting the Outcome

Proceedings of the 12th International Joint Conference on Biomedical Engineering Systems and Technologies ◽

10.5220/0007355200420051 ◽

2019 ◽

Cited By ~ 1

Author(s):

Rodrigo Lima ◽

Daniel Osório ◽

Hugo Gamboa

Keyword(s):

Heart Rate ◽

Heart Rate Variability ◽

Mental Stress ◽

Electrodermal Activity

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Heart Rate Variability and Electrodermal Activity as Noninvasive Indices of Sympathovagal Balance in Response to Stress

Acta Medica Martiniana ◽

10.2478/acm-2013-0006 ◽

2013 ◽

Vol 13 (1) ◽

pp. 5-13 ◽

Cited By ~ 7

Author(s):

Zuzana Visnovcova ◽

A. Calkovska ◽

I. Tonhajzerova

Keyword(s):

Heart Rate ◽

Heart Rate Variability ◽

Nervous System ◽

Autonomic Nervous System ◽

Mental Stress ◽

Electrodermal Activity ◽

Regulatory System ◽

Sympathovagal Balance ◽

Autonomic Nervous ◽

Response To Stress

Abstract The autonomic nervous system (ANS) is a principal regulatory system for maintaining homeostasis, adaptability and physiological flexibility of the organism at rest as well as in response to stress. In the aspect of autonomic regulatory inputs on the cardiovascular system, recent research is focused on the study of exaggerated/diminished cardiovascular reactivity in response to mental stress as a risk factor for health complications, e.g. hypertension. Thus, the analysis of biological signals reflecting a physiological shift in sympathovagal balance during stress in the manner of vagal withdrawal associated with sympathetic overactivity is important. The heart rate variability, i.e. “beat-to-beat” oscillations of heart rate around its mean value, reflects mainly complex neurocardiac parasympathetic control. The electrodermal activity could represent “antagonistic” sympathetic activity, the so-called “sympathetic arousal” in response to stress. The detailed study of the physiological parameters under various stressful stimuli and in recovery phase using traditional and novel mathematical analyses could reveal discrete alterations in sympathovagal balance. This article summarizes the importance of heart rate variability and electrodermal activity assessment as the potential noninvasive indices indicating autonomic nervous system activity in response to mental stress.

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