The Three Arousal Model: Implications of Gray's Two-Factor Learning Theory for Heart Rate, Electrodermal Activity, and Psychopathy

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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Quantifying Occupational Stress in Intensive Care Unit Nurses: An Applied Naturalistic Study of Correlations Among Stress, Heart Rate, Electrodermal Activity, and Skin Temperature

Human Factors The Journal of the Human Factors and Ergonomics Society ◽

10.1177/00187208211040889 ◽

2021 ◽

pp. 001872082110408

Author(s):

Nima Ahmadi ◽

Farzan Sasangohar ◽

Tariq Nisar ◽

Valerie Danesh ◽

Ethan Larsen ◽

...

Keyword(s):

Intensive Care Unit ◽

Heart Rate ◽

Intensive Care ◽

Skin Temperature ◽

Electrodermal Activity ◽

Laboratory Research ◽

Prior Literature ◽

High Level ◽

Intensive Care Unit Nurses ◽

Skin Temperatures

Objective To identify physiological correlates to stress in intensive care unit nurses. Background Most research on stress correlates are done in laboratory environments; naturalistic investigation of stress remains a general gap. Method Electrodermal activity, heart rate, and skin temperatures were recorded continuously for 12-hr nursing shifts (23 participants) using a wrist-worn wearable technology (Empatica E4). Results Positive correlations included stress and heart rate (ρ = .35, p < .001), stress and skin temperature (ρ = .49, p < .05), and heart rate and skin temperatures (ρ = .54, p = .0008). Discussion The presence and direction of some correlations found in this study differ from those anticipated from prior literature, illustrating the importance of complementing laboratory research with naturalistic studies. Further work is warranted to recognize nursing activities associated with a high level of stress and the underlying reasons associated with changes in physiological responses. Application Heart rate and skin temperature may be used for real-time detection of stress, but more work is needed to validate such surrogate measures.

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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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Five New Things: Seizure Detection Devices

Neurology Clinical Practice ◽

10.1212/cpj.0000000000001044 ◽

2021 ◽

pp. 10.1212/CPJ.0000000000001044

Author(s):

Alexandra Carrick Atwood ◽

Cornelia Natasha Drees

Keyword(s):

Heart Rate ◽

Motion Detection ◽

Electrodermal Activity ◽

False Positive Rate ◽

Seizure Detection ◽

Mechanisms Of Action ◽

Unexpected Death ◽

Positive Rate ◽

Convulsive Seizures ◽

Electroencephalogram Eeg

ABSTRACT:Purpose: The purpose of this paper is to review seizure detection devices, their mechanisms of action, efficacy and reflecting upon potential improvements for future devices.Recent Findings: There are five main categories of seizure detection devices ([email protected]), these include electroencephalogram (EEG), heart rate detection (HR), electrodermal activity (EDA), motion detection and electromyography (EMG). These devices can be used in combination or in isolation to detect seizures. These devices are high in their sensitivity for convulsive seizures, but low in specificity because of a tendency to detect artifact. Overall, they perform poorly identifying non-convulsive seizures.Summary: Seizure detection devices are currently most useful in detecting convulsive seizures and thereby might help against sudden unexpected death in epilepsy (SUDEP), though they have a high false positive rate. These devices are much less adept at detecting more clinically subtle seizures.

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Physiological Synchrony in EEG, Electrodermal Activity and Heart Rate Detects Attentionally Relevant Events in Time

Frontiers in Neuroscience ◽

10.3389/fnins.2020.575521 ◽

2020 ◽

Vol 14 ◽

Author(s):

Ivo V. Stuldreher ◽

Nattapong Thammasan ◽

Jan B. F. van Erp ◽

Anne-Marie Brouwer

Keyword(s):

Heart Rate ◽

Stimulus Type ◽

Electrodermal Activity ◽

Physiological Signals ◽

Independent Group ◽

Top Down ◽

Event Type ◽

Physiological Synchrony ◽

Electroencephalogram Eeg ◽

Over Time

Interpersonal physiological synchrony (PS), or the similarity of physiological signals between individuals over time, may be used to detect attentionally engaging moments in time. We here investigated whether PS in the electroencephalogram (EEG), electrodermal activity (EDA), heart rate and a multimodal metric signals the occurrence of attentionally relevant events in time in two groups of participants. Both groups were presented with the same auditory stimulus, but were instructed to attend either to the narrative of an audiobook (audiobook-attending: AA group) or to interspersed emotional sounds and beeps (stimulus-attending: SA group). We hypothesized that emotional sounds could be detected in both groups as they are expected to draw attention involuntarily, in a bottom-up fashion. Indeed, we found this to be the case for PS in EDA or the multimodal metric. Beeps, that are expected to be only relevant due to specific “top-down” attentional instructions, could indeed only be detected using PS among SA participants, for EDA, EEG and the multimodal metric. We further hypothesized that moments in the audiobook accompanied by high PS in either EEG, EDA, heart rate or the multimodal metric for AA participants would be rated as more engaging by an independent group of participants compared to moments corresponding to low PS. This hypothesis was not supported. Our results show that PS can support the detection of attentionally engaging events over time. Currently, the relation between PS and engagement is only established for well-defined, interspersed stimuli, whereas the relation between PS and a more abstract self-reported metric of engagement over time has not been established. As the relation between PS and engagement is dependent on event type and physiological measure, we suggest to choose a measure matching with the stimulus of interest. When the stimulus type is unknown, a multimodal metric is most robust.

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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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Evaluating physiological signal salience for estimating metabolic energy cost from wearable sensors

Journal of Applied Physiology ◽

10.1152/japplphysiol.00714.2018 ◽

2019 ◽

Vol 126 (3) ◽

pp. 717-729 ◽

Cited By ~ 6

Author(s):

Kimberly A. Ingraham ◽

Daniel P. Ferris ◽

C. David Remy

Keyword(s):

Heart Rate ◽

Steady State ◽

Indirect Calorimetry ◽

Energy Cost ◽

Electrodermal Activity ◽

Wearable Sensors ◽

Metabolic Energy ◽

Physiological Signals ◽

Physiological Signal ◽

Signal Salience

Body-in-the-loop optimization algorithms have the capability to automatically tune the parameters of robotic prostheses and exoskeletons to minimize the metabolic energy expenditure of the user. However, current body-in-the-loop algorithms rely on indirect calorimetry to obtain measurements of energy cost, which are noisy, sparsely sampled, time-delayed, and require wearing a respiratory mask. To improve these algorithms, the goal of this work is to predict a user’s steady-state energy cost quickly and accurately using physiological signals obtained from portable, wearable sensors. In this paper, we quantified physiological signal salience to discover which signals, or groups of signals, have the best predictive capability when estimating metabolic energy cost. We collected data from 10 healthy individuals performing 6 activities (walking, incline walking, backward walking, running, cycling, and stair climbing) at various speeds or intensities. Subjects wore a suite of physiological sensors that measured breath frequency and volume, limb accelerations, lower limb EMG, heart rate, electrodermal activity, skin temperature, and oxygen saturation; indirect calorimetry was used to establish the ‘ground truth’ energy cost for each activity. Evaluating Pearson’s correlation coefficients and single and multiple linear regression models with cross validation (leave-one- subject-out and leave-one- task-out), we found that 1) filtering the accelerations and EMG signals improved their predictive power, 2) global signals (e.g., heart rate, electrodermal activity) were more sensitive to unknown subjects than tasks, while local signals (e.g., accelerations) were more sensitive to unknown tasks than subjects, and 3) good predictive performance was obtained combining a small number of signals (4–5) from multiple sensor modalities. NEW & NOTEWORTHY In this paper, we systematically compare a large set of physiological signals collected from portable sensors and determine which sensor signals contain the most salient information for predicting steady-state metabolic energy cost, robust to unknown subjects or tasks. This information, together with the comprehensive data set that is published in conjunction with this paper, will enable researchers and clinicians across many fields to develop novel algorithms to predict energy cost from wearable sensors.

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