Applying artificial intelligence on EDA sensor data to predict stress on minimally invasive robotic-assisted surgery.

PURPOSE: This study aims predicting the stress level based on the ergonomic (kinematic) and physiological (electrodermal activity-EDA, blood pressure and body temperature) parameters of the surgeon from their records collected in the previously immediate situation of a minimally invasive robotic surgery activity. METHODS: For this purpose, data related to the surgeon's ergonomic and physiological parameters were collected during twenty-six robotic-assisted surgical sessions completed by eleven surgeons with different experience levels. Once the dataset was generated, two preprocessing techniques were applied (scaled and normalized), these two datasets were divided into two subsets: with 80% of data for training and cross-validation, and 20% of data for test. Three predictive techniques (multiple linear regression-MLR, support vector machine-SVM and multilayer perceptron-MLP) were applied on training dataset to generate predictive models. Finally, these models were validated on cross-validation and test datasets. After each session, surgeons were asked to complete a survey of their feeling of stress. These data were compared with those obtained using predictive models. RESULTS: The results showed that MLR combined with the scaled preprocessing achieved the highest R2 coefficient and the lowest error for each parameter analyzed. Additionally, the results for the surgeons' surveys were highly correlated to the results obtained by the predictive models (R2 = 0.8253). CONCLUSIONS: The linear models proposed in this study were successfully validated on cross-validation and test datasets. This fact demonstrates the possibility of predicting factors that help us to improve the surgeon's health during robotic surgery.

Sleep-wake variations of electrodermal activity in bipolar disorder.

BACKGROUND: Affective states influence the sympathetic nervous system, inducing variations in electrodermal activity (EDA), however, EDA association with bipolar disorder (BD) remains uncertain in real-world settings due to confounders like physical activity and temperature. We analysed EDA separately during sleep and wakefulness due to varying confounders and potential differences in mood state discrimination capacities. METHODS: We monitored EDA from 102 participants with BD including 35 manic, 29 depressive, 38 euthymic patients, and 38 healthy controls (HC), for 48 h. Fifteen EDA features were inferred by mixed-effect models for repeated measures considering sleep state, group and covariates. RESULTS: Thirteen EDA feature models were significantly influenced by sleep state, notably including phasic peaks (p < 0.001). During wakefulness, phasic peaks showed different values for mania (M [SD] = 6.49 [5.74, 7.23]), euthymia (5.89 [4.83, 6.94]), HC (3.04 [1.65, 4.42]), and depression (3.00 [2.07, 3.92]). Four phasic features during wakefulness better discriminated between HC and mania or euthymia, and between depression and euthymia or mania, compared to sleep. Mixed symptoms, average skin temperature, and anticholinergic medication affected the models, while sex and age did not. CONCLUSION: EDA measured from awake recordings better distinguished between BD states than sleep recordings, when controlled by confounders.

Zooming into the Complex Dynamics of Electrodermal Activity Recorded during Emotional Stimuli: A Multiscale Approach.

Physiological phenomena exhibit complex behaviours arising at multiple time scales. To investigate them, techniques derived from chaos theory were applied to physiological signals, providing promising results in distinguishing between healthy and pathological states. Fractal-like properties of electrodermal activity (EDA), a well-validated tool for monitoring the autonomic nervous system state, have been reported in previous literature. This study proposes the multiscale complexity index of electrodermal activity (MComEDA) to discern different autonomic responses based on EDA signals. This method builds upon our previously proposed algorithm, ComEDA, and it is empowered with a coarse-graining procedure to provide a view at multiple time scales of the EDA response. We tested MComEDA's performance on the EDA signals of two publicly available datasets, i.e., the Continuously Annotated Signals of Emotion (CASE) dataset and the Affect, Personality and Mood Research on Individuals and Groups (AMIGOS) dataset, both containing physiological data recorded from healthy participants during the view of ultra-short emotional video clips. Our results highlighted that the values of MComEDA were significantly different (p-value < 0.05 after Wilcoxon signed rank test with Bonferroni's correction) when comparing high- and low-arousal stimuli. Furthermore, MComEDA outperformed the single-scale approach in discriminating among different valence levels of high-arousal stimuli, e.g., showing significantly different values for scary and amusing stimuli (p-value = 0.024). These findings suggest that a multiscale approach to the nonlinear analysis of EDA signals can improve the information gathered on task-specific autonomic response, even when ultra-short time series are considered.

Non-invasive detection of mental fatigue in construction equipment operators through geometric measurements of facial features.

INTRODUCTION: Prolonged operation of construction equipment could lead to mental fatigue, which can increase the chances of human error-related accidents as well as operators' ill-health. The objective detection of operators' mental fatigue is crucial for reducing accident risk and ensuring operator health. Electroencephalography, photoplethysmography, electrodermal activity, and eye-tracking technology have been used to mitigate this issue. These technologies are invasive and wearable sensors that can cause irritation and discomfort. Geometric measurements of facial features can serve as a noninvasive alternative approach. Its application in detecting mental fatigue of construction equipment operators has not been reported in the literature. Although the application of facial features has been widespread in other domains, such as drivers and other occupation scenarios, their ecological validity for construction excavator operators remains a knowledge gap. METHOD: This study proposed employing geometric measurements of facial features to detect mental fatigue in construction equipment operators' facial features. In this study, seventeen operators performed excavation operations. Mental fatigue was labeled subjectively and objectively using NASA-TLX scores and EDA values. Based on geometric measurements, facial features (eyebrow, mouth outer, mouth corners, head motion, eye area, and face area) were extracted. RESULTS: The results showed that there was significant difference in the measured metrics for high fatigue compared to low fatigue. Specifically, the most noteworthy variation was for the eye and face area metrics, with mean differences of 45.88% and 26.9%, respectively. CONCLUSIONS: The findings showed that geometrical measurements of facial features are a useful, noninvasive approach for detecting the mental fatigue of construction equipment operators.

Haptic coordination: Squeezing a vibrating stress ball decreases anxiety and arousal.

We evaluated the effect of haptic coordination on anxiety and arousal. Participants looked at a stressful or calming image and then repeatedly squeezed a vibrating stress ball for 20 s. Using a pre-post paradigm with a control group, we showed that squeezing the vibrating ball reduced anxiety and arousal, as assessed by the State-Trait Anxiety Inventory and electrodermal activity, respectively. The stability of haptic coordination was manipulated by varying the detuning between the spontaneous squeezing frequency and the intrinsic frequency of ball vibration. Coordination stability affected arousal and stress affected stability. The data were discussed in the light of Kahneman's attentional resource-sharing model.

Sympathetic arousal among depressed college students: Examining the interplay between psychopathology and social activity.

Depressed individuals exhibit altered sensitivity to both positive and negative social contact, and may not reap the same psychological and emotional benefits to socializing as non-depressed individuals. Although depressive symptoms and loneliness predict social withdrawal and decreased pleasure, little is currently understood about immediate affective arousal dynamics during real-time socializing. Using a novel ambulatory protocol that tracked both objective features of affective arousal (electrodermal activity) and subjective valence (self-reported) during college students' social interactions, we evaluated the moderating role of depression and loneliness symptoms on the associations between socializing with others (specifically, with a romantic partner, a close friend, or a group of friends) and the arousal and valence dimensions of affect. Among a racially and ethnically diverse sample of 118 college students (64% African American/Black/Continental African, 20% Latinx, 8% Asian, and 8% White) recruited from a large, predominantly White Midwestern university, those lower in depression and loneliness symptomatology evinced decreased average arousal (Β = -0.10, SE = 0.04, p < .01) when in relaxed and intimate socializing contexts (e.g., with a romantic partner and a close friend), consistent with the idea that these contexts facilitate important opportunities for psychological rest and recovery. Those lower in depression and loneliness symptoms also showed higher average arousal when socializing in the energizing context of being with a group of friends. Overall, the results suggest psychopathology is reflected in patterns of sympathetic arousal when socializing, with more depressed and lonely individuals generally feeling worse while receiving fewer psychophysiological rewards in multiple socializing contexts.

Human Activity Recognition Algorithm with Physiological and Inertial Signals Fusion: Photoplethysmography, Electrodermal Activity, and Accelerometry.

Inertial signals are the most widely used signals in human activity recognition (HAR) applications, and extensive research has been performed on developing HAR classifiers using accelerometer and gyroscope data. This study aimed to investigate the potential enhancement of HAR models through the fusion of biological signals with inertial signals. The classification of eight common low-, medium-, and high-intensity activities was assessed using machine learning (ML) algorithms, trained on accelerometer (ACC), blood volume pulse (BVP), and electrodermal activity (EDA) data obtained from a wrist-worn sensor. Two types of ML algorithms were employed: a random forest (RF) trained on features; and a pre-trained deep learning (DL) network (ResNet-18) trained on spectrogram images. Evaluation was conducted on both individual activities and more generalized activity groups, based on similar intensity. Results indicated that RF classifiers outperformed corresponding DL classifiers at both individual and grouped levels. However, the fusion of EDA and BVP signals with ACC data improved DL classifier performance compared to a baseline DL model with ACC-only data. The best performance was achieved by a classifier trained on a combination of ACC, EDA, and BVP images, yielding F1-scores of 69 and 87 for individual and grouped activity classifications, respectively. For DL models trained with additional biological signals, almost all individual activity classifications showed improvement (p-value < 0.05). In grouped activity classifications, DL model performance was enhanced for low- and medium-intensity activities. Exploring the classification of two specific activities, ascending/descending stairs and cycling, revealed significantly improved results using a DL model trained on combined ACC, BVP, and EDA spectrogram images (p-value < 0.05).

Lateral asymmetry of emotional arousal as a biomarker of attributional style during darts competitions.

According to the Multiple Arousal Theory, electrodermal activity (EDA) is not uniform across the body. However, the psychological meaning of a left or right-sided EDA dominance is still not clear. We explored EDA lateral asymmetry as a psychophysiological marker of optimistic and pessimistic attributional style regarding success and failure in a darts competition. Bilateral EDA pattern of 230 throws of a competing pair was measured by Obimon EDA including accelerometer measurements of movements. First, we confirmed that lateral asymmetry can be measured reliably based on EDA data from both wrists. Second, we assessed attributional styles related to lateral asymmetry based on 80 individual throws. We recorded participants' expectations regarding their upcoming performance, and their attribution of success and failure based on Seligman's definition as optimist (internal cause attributed to success, or external cause ascribed to failure) or pessimist. The ratio of optimist and pessimist attributions was significantly different for throws with right or left-sided EDA dominance (p = 0.001). Optimistic attribution characterized 84% of right dominant, while pessimist 63% of left-dominant EDA during throws. We replicated these findings on 50 throws from 10 more individuals (p = 0.034). All individuals were right-handed. We conclude that wrist EDA can be reliably measured during physical movements, such as in a darts game. Lateral EDA asymmetry is a consistent psychophysiological marker of the attitude toward success and failure in a competitive setting, suggesting that lateral asymmetry of emotional arousal may serve as a novel psychophysiological biomarker for attribution style. Results underlie the psychophysiological relevance of bilateral arousal assessment and provide evidence-based verification for the Multiple Arousal Theory.

Body shape rather than facial emotion of others alters interpersonal distance in patients with anorexia nervosa.

OBJECTIVE: The study investigated interpersonal distance in patients with anorexia nervosa (AN), focussing on the role of other's facial expression and morphology, also assessing physiological and subjective responses. METHOD: Twenty-nine patients with AN and 30 controls (CTL) were exposed to virtual characters either with an angry, neutral, or happy facial expression or with an overweight, normal-weight, or underweight morphology presented either in the near or far space while we recorded electrodermal activity. Participants had to judge their preferred interpersonal distance with the characters and rated them in terms of valence and arousal. RESULTS: Unlike CTL, patients with AN exhibited heightened electrodermal activity for morphological stimuli only, when presented in the near space. They also preferred larger and smaller interpersonal distances with overweight and underweight characters respectively, although rating both negatively. Finally, and similar to CTL, they preferred larger interpersonal distance with angry than neutral or happy characters. DISCUSSION: Although patients with AN exhibited behavioural response to emotional stimuli similar to CTL, they lacked corresponding physiological response, indicating emotional blunting towards emotional social stimuli. Moreover, they showed distinct behavioural and physiological adjustments in response to body shape, confirming the specific emotional significance attached to body shape.

Exploring physiological stress response evoked by passive translational acceleration in healthy adults: a pilot study utilizing electrodermal activity and heart rate variability measurements.

Passive translational acceleration (PTA) has been demonstrated to induce the stress response and regulation of autonomic balance in healthy individuals. Electrodermal activity (EDA) and heart rate variability (HRV) measurements are reliable indicators of the autonomic nervous system (ANS) and can be used to assess stress levels. The objective of this study was to investigate the potential of combining EDA and HRV measurements in assessing the physiological stress response induced by PTA. Fourteen healthy subjects were randomly assigned to two groups of equal size. The experimental group underwent five trials of elevator rides, while the control group received a sham treatment. EDA and HRV indices were obtained via ultra-short-term analysis and compared between the two groups to track changes in the ANS. In addition, the complexity of the EDA time series was compared between the 4 s before and the 2-6 s after the onset of PTA to assess changes in the subjects' stress levels in the experimental group. The results revealed a significant increase in the skin conductance response (SCR) frequency and a decrease in the root mean square of successive differences (RMSSD) and high frequency (HF) components of HRV. In terms of stress assessment, the results showed an increase in the complexity of the EDA time series 2-6 s after the onset of PTA. These results indicate an elevation in sympathetic tone when healthy subjects were exposed to a translational transport scenario. Furthermore, evidence was provided for the ability of EDA complexity to differentiate stress states in individual trials of translational acceleration.

Electrodermal and central measures of the tonic orienting reflex (OR).

Sokolov described both phasic and tonic aspects of the Orienting Reflex (OR), but subsequent research and theory development has focussed primarily on the phasic OR at the expense of the tonic OR. The present study used prestimulus skin conductance level (SCL) during a dishabituation paradigm to model the tonic OR, examining its amplitude patterning over repeated standard stimulus presentations and a change stimulus. We expected sensitisation (increased amplitude) following the initial and change trials, and habituation (decrement) over the intervening trials. Prestimulus EEG alpha level was explored as a potential central measure of the tonic OR (as an inverse correlate), examining its pattern over stimulus repetition and change in relation to the SCL model. We presented a habituation series of innocuous auditory stimuli to two groups (each N = 20) at different ISIs (Long 13-15 s and Short 5-7 s) and recorded electrodermal and EEG data during two counterbalanced conditions; Indifferent: no task requirements; Significant: silent counting. Across groups and conditions, prestimulus SCLs and alpha amplitudes generally showed the expected trials patterns, confirming our main hypotheses. Findings have important implications for including the assessment of Sokolov's tonic OR in modelling central and autonomic nervous system interactions of fundamental attention and learning processes.

Effects of trace element dietary supplements on voice parameters and some physiological and psychological parameters related to stress.

Trace elements, often used as dietary supplements, are widely accessible without prescription at pharmacies. Pronutri has pioneered Nutripuncture®, a methodology that utilizes orally consumed trace elements to elicit a physiological response akin to that of acupuncture. Pronutri has empirically observed that the user's voice becomes deeper following an exclusive ingestion procedure. Given that alterations in vocal characteristics are often linked to stress, the Pronutri researchers postulated that the pills have the capacity to promptly alleviate stress upon ingestion. Nevertheless, there is a lack of scientific substantiation about the impact of these supplements on voice (or stress) indicators. The aim of this research was to determine whether there is a consistent impact of trace element ingestion on vocal characteristics, namely the fundamental frequency of the voice, as well as other physiological and psychological stress measurements. In order to achieve this objective, we have devised a unique methodology to examine this hypothesis. This involves conducting a monocentric crossover, randomized, triple-blind, placebo-controlled trial with a sample size of 43 healthy individuals. This study demonstrates that compared to placebo tablets, consuming 10 metal traces containing tablets at once is enough to cause noticeable changes in the vocal spectrum in the direction of an improvement of the voice timbre "richness", and a decrease in the occurrence of spontaneous electrodermal activity, suggesting a stress reduction. However, there were no significant changes observed in the other parameters that were tested. These parameters include vocal measures such as voice frequency F0, standard deviation from this frequency, jitter, and shimmer. Additionally, physiological measures such as respiratory rate, oxygenation and heart rate variability parameters, as well as psychological measures such as self-assessment analogic scales of anxiety, stress, muscle tension, and nervous tension, did not show any significant changes. Ultimately, our research revealed that the ingestion of 10 trace elements pills may promptly elicit a targeted impact on both vocal spectrum and electrodermal activity. Despite the limited impact, these findings warrant more research to explore the long-term effects of trace elements on voice and stress reduction.

Elevator music as a tool for the quantitative characterization of reward.

While certain musical genres and songs are widely popular, there is still large variability in the music that individuals find rewarding or emotional, even among those with a similar musical enculturation. Interestingly, there is one Western genre that is intended to attract minimal attention and evoke a mild emotional response: elevator music. In a series of behavioral experiments, we show that elevator music consistently elicits low pleasure and surprise. Participants reported elevator music as being less pleasurable than music from popular genres, even when participants did not regularly listen to the comparison genre. Participants reported elevator music to be familiar even when they had not explicitly heard the presented song before. Computational and behavioral measures of surprisal showed that elevator music was less surprising, and thus more predictable, than other well-known genres. Elevator music covers of popular songs were rated as less pleasurable, surprising, and arousing than their original counterparts. Finally, we used elevator music as a control for self-selected rewarding songs in a proof-of-concept physiological (electrodermal activity and piloerection) experiment. Our results suggest that elevator music elicits low emotional responses consistently across Western music listeners, making it a unique control stimulus for studying musical novelty, pleasure, and surprise.

Associations between social anxiety, physiological reactivity, and speech disfluencies in autistic young adults and controls.

INTRODUCTION: The aim of this study was to examine possible associations of social anxiety (SA) and speaking-related physiological reactivity with the frequencies of a) total disfluencies, b) typical disfluencies, and c) stuttering-like disfluencies, as well as d) stuttering-severity in autistic young adults and controls. METHODS: Thirty-two autistic young adults and 35 controls participated in this study. Participants were presented with video clips (viewing condition) and were then asked to talk about the videos (narrating condition). SA was measured by the self-report Social Phobia and Anxiety Inventory (SPAI). Speaking-related physiological reactivity was measured by the electrodermal activity (EDA), an index of emotional arousal. The speech samples from the narrating condition were analyzed for type and frequency of speech disfluencies and used for determining the stuttering severity. SA and speaking-related physiological reactivity were compared between the groups. Correlation between SA, physiological reactivity, disfluency frequencies, and stuttering severity were tested separately for both groups. RESULTS: No between-group differences were found in the overall SA, yet differences were found in SPAI subscales of social interaction, group interaction, and avoidance, as well as in agoraphobia. Both groups had higher physiological arousal in narrating condition in comparison to the video viewing condition, yet there was no between-group difference in the reactivity. No associations were found between SPAI measures, physiological reactivity, disfluency frequencies, and stuttering severity in the autistic group. In the control group, a negative association was found between physiological reactivity and total and typical disfluency frequencies. CONCLUSIONS: SA or speaking-related physiological reactivity were not associated with disfluency frequencies or stuttering severity in autistic persons. Negative association between physiological reactivity and disfluency frequencies found in the control group may indicate that the physiological arousal may impact the speech production process by reducing the overt disfluencies.

Integrating smartwatches in community mental health services for severe mental illness for detecting relapse and informing future intervention: A case series.

OBJECTIVE: This case series explored the integration of smartwatches in a community mental health service to support severe mental illness (SMI) management and intervention. We examined whether biometric data provided by smartwatches could help to predict relapse and inform treatment decisions. METHOD: Four Australian SMI outpatients of mixed diagnoses (age range = 19-24) were selected from a prior study. Clinicians accessed patients' biometric data (activity, sleep, heart rate, and electrodermal activity) through smartwatches. RESULTS: Changes in circadian rhythm and electrodermal activity preceded hospitalization in two cases. Additionally, smartwatch data was effectively used to guide targeted interventions, improving patient treatment outcomes. CONCLUSION: Integrating smartwatches in community mental health services offers promise as adjunct tools for SMI management. However, ethical considerations on data privacy and technology reliance require further evaluation. Additionally, as this is a small case series, randomized controlled trials with larger sample sizes are required to provide evidence for generalisability of results.

Application of DIY Electrodermal Activity Wristband in Detecting Stress and Affective Responses of Students.

This paper describes the analysis of electrodermal activity (EDA) in the context of students' scholastic activity. Taking a multidisciplinary, citizen science and maker-centric approach, low-cost, bespoken wearables, such as a mini weather station and biometric wristband, were built. To investigate both physical health as well as stress, the instruments were first validated against research grade devices. Following this, a research experiment was created and conducted in the context of students' scholastic activity. Data from this experiment were used to train machine learning models, which were then applied to interpret the relationships between the environment, health, and stress. It is hoped that analyses of EDA data will further strengthen the emerging model describing the intersections between local microclimate and physiological and neurological stress. The results suggest that temperature and air quality play an important role in students' physiological well-being, thus demonstrating the feasibility of understanding the extent of the effects of various microclimatic factors. This highlights the importance of thermal comfort and air ventilation in real-life applications to improve students' well-being. We envision our work making a significant impact by showcasing the effectiveness and feasibility of inexpensive, self-designed wearable devices for tracking microclimate and electrodermal activity (EDA). The affordability of these wearables holds promising implications for scalability and encourages crowd-sourced citizen science in the relatively unexplored domain of microclimate's influence on well-being. Embracing citizen science can then democratize learning and expedite rapid research advancements.

Unveiling gender differences in psychophysiological dynamics: support for a two-dimensional autonomic space approach.

Introduction: To date, studies focusing on the connection between psychological functioning and autonomic nervous system (ANS) activity usually adopted the one-dimensional model of autonomic balance, according to which activation of one branch of the ANS is accompanied by an inhibition of the other. However, the sympathetic and parasympathetic branches also activate independently; thus, co-activation and co-inhibition may occur, which is demonstrated by a two-dimensional model of ANS activity. Here, we apply such models to assess how markers of the autonomic space relate to several critical psychological constructs: emotional contagion (EC), general anxiety, and positive and negative affect (PA and NA). We also examined gender differences in those psychophysiological relations. Methods: In the present study, we analyzed data from 408 healthy students, who underwent a 5-min group baseline period as part of their participation in several experiments and completed self-reported questionnaires. Electrocardiogram (ECG), electrodermal activity (EDA), and respiration were recorded. Respiratory sinus arrhythmia (RSA), pre-ejection period (PEP), as well as cardiac autonomic balance (CAB) and regulation (CAR) and cross-system autonomic balance (CSAB) and regulation (CSAR), were calculated. Results: Notably, two-dimensional models were more suitable for predicting and describing most psychological constructs. Gender differences were found in psychological and physiological aspects as well as in psychophysiological relations. Women's EC scores were negatively correlated with sympathetic activity and positively linked to parasympathetic dominance. Men's PA and NA scores were positively associated with sympathetic activity. PA in men also had a positive link to an overall activation of the ANS, and a negative link to parasympathetic dominance. Discussion: The current results expand our understanding of the psychological aspects of the autonomic space model and psychophysiological associations. Gender differences and strengths and weaknesses of alternative physiological models are discussed.

Effect of Acute Ketamine Treatment on Sympathetic Regulation Indexed by Electrodermal Activity in Adolescent Major Depression.

Ketamine is a potential rapid-onset antidepressant characterized by sympathomimetic effects. However, the question of ketamine's use in treating adolescents' major depressive disorder (MDD) is still discussed. Thus, we aimed to study the acute effect of ketamine infusion treatment on sympathetic regulation using electrodermal activity (EDA) in addition to an assessment of depressive symptomatology in MDD adolescents. Twenty hospitalized adolescent girls with MDD (average age: 15.0 ± 1.46 yrs.) were examined before and two hours after a single intravenous infusion of ketamine. EDA was continuously recorded for 6 min, and depressive symptoms were assessed before and two hours after ketamine administration. The evaluated parameters included skin conductance level (SCL), nonspecific electrodermal responses (NS-SCRs), MADRS (questions no. 1-10, total score), and CDI (items A-E, total score). EDA parameters showed no significant changes after the ketamine treatment, and depressive symptoms were significantly reduced after the ketamine infusion. The analysis revealed a significant negative correlation between index SCL and CDI-A, CDI-E, and the total CDI score and between index NS-SCRs and MADRS no. 4 before the ketamine treatment. In conclusion, ketamine improved depressive symptomatology without a significant effect on EDA, indicating its potential safety and efficiency as an acute antidepressant intervention in adolescent MDD.

Changes in electrodermal activity following sympathicotomy in hyperhidrosis patients.

Objectives: The aim of this study was to assess the potential of electrodermal activity (EDA) as a diagnostic tool for preoperative evaluation in hyperhidrosis patients. EDA levels and patterns in different skin areas were investigated before and after endoscopic thoracic sympathicotomy (ETS) and was compared to healthy subjects. Methods: Thirty-seven patients underwent two days of measurements before and after the operation. Twenty-five (67.5%) of the patients also had a third measurement after six months. Non-invasive EDA measurements, involving skin conductance, were sampled from five different skin areas while patients were at rest in supine and sitting positions or when subjected to stimuli such as deep inspirations, mental challenge, and exposure to a sudden loud sound. Results: Prior to the operation, hyperhidrosis patients showed higher spontaneous palm EDA variations at rest and stronger responses to stimuli compared to healthy subjects. Patients with facial blushing/hyperhidrosis or combined facial/palmar hyperhidrosis showed minimal spontaneous activity or responses, particularly during mental challenge and sound stimulus. Notably, palm EDA response was abolished shortly following sympathicotomy, although a minor response was observed after six months. Minimal EDA responses were also observed in the back and abdomen postoperatively. Conclusion: Hyperhidrosis patients showed stronger EDA response to stimuli compared to healthy subjects. Sympathicotomy resulted in the complete elimination of palm EDA responses, gradually returning to a limited extent after six months. These findings suggest that EDA recordings could be utilized in preoperative assessment of hyperhidrosis patients.

Optimal Electrodermal Activity Segment for Enhanced Emotion Recognition Using Spectrogram-Based Feature Extraction and Machine Learning.

In clinical and scientific research on emotion recognition using physiological signals, selecting the appropriate segment is of utmost importance for enhanced results. In our study, we optimized the electrodermal activity (EDA) segment for an emotion recognition system. Initially, we obtained EDA signals from two publicly available datasets: the Continuously annotated signals of emotion (CASE) and Wearable stress and affect detection (WESAD) for 4-class dimensional and three-class categorical emotional classification, respectively. These signals were pre-processed, and decomposed into phasic signals using the 'convex optimization to EDA' method. Further, the phasic signals were segmented into two equal parts, each subsequently segmented into five nonoverlapping windows. Spectrograms were then generated using short-time Fourier transform and Mel-frequency cepstrum for each window, from which we extracted 85 features. We built four machine learning models for the first part, second part, and whole phasic signals to investigate their performance in emotion recognition. In the CASE dataset, we achieved the highest multi-class accuracy of 62.54% using the whole phasic and 61.75% with the second part phasic signals. Conversely, the WESAD dataset demonstrated superior performance in three-class emotions classification, attaining an accuracy of 96.44% for both whole phasic and second part phasic segments. As a result, the second part of EDA is strongly recommended for optimal outcomes.