Heart Rate Variability and Electrodermal Activity Biosignal Processing: Predicting the Autonomous Nervous System Response in Mental Stress

Lima, Rodrigo Monteiro de; Osório, Daniel; Gamboa, Hugo

doi:10.1007/978-3-030-46970-2_16

Cited by 7 publications

(7 citation statements)

References 39 publications

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“…The study of the Autonomic Nervous System (ANS) in the assessment of psychological stress is well established. 39 Throughout history, stress has been defined as a shift from a state of calm to one of heightened alertness, all in the pursuit of safeguarding an organism's well-being. 40 This concept traces back to early 450 BC, when Empedocles conceptualized stress as a threat to the harmonious balance of an organism's vital elements.…”

Section: Ans As the Gateway To Understanding Mental Healthmentioning

confidence: 99%

Biomarkers of Anxiety and Depression – A Novel Method of Tracking the Autonomic Nervous System with Machine Learning

Borthakur,

Sharma,

Tank

et al. 2024

Preprint

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This study presents a novel method to understand biomarkers of anxiety and depression by using a concept called Sympathetic Transition Points (STP), which is indicative of Autonomic Nervous System (ANS) dynamics. Wearables-based Electrodermal Activity (EDA) and Blood Volume Pulse (BVP) data were collected from 61 controls, 60 individuals with depressive symptoms, and 110 individuals with anxiety. By monitoring ANS activity, patterns related to anxiety and mood states and their transitions in real-time were identified, using machine learning. Analysis revealed clear distinctions between groups and enabled tracking of mental state changes. A score of .99 F1, with an ROC of 1 was achieved in automatically classifying anxiety, depression, and neutral states with this method. The method lays the groundwork for automated mental health assessments in real-world settings, introducing an efficient and objective screening protocol. By utilizing wearable technology, machine learning, and ANS monitoring, this work advocates for improved early detection and intervention strategies in mental healthcare.

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Section: Ans As the Gateway To Understanding Mental Healthmentioning

confidence: 99%

Biomarkers of Anxiety and Depression – A Novel Method of Tracking the Autonomic Nervous System with Machine Learning

Borthakur,

Sharma,

Tank

et al. 2024

Preprint

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“…By integrating these signals and employing advanced algorithms, their study aimed to enhance the recognition of emotions. Additionally, Lima et al (2020) presented their research on mental stress prediction. They combined PPG and GSR signals, which were collected using their own prototype equipped with PPG and GSR sensors.…”

Section: Single-modality and Multi-modalities Ersmentioning

confidence: 99%

Emotion recognition with multi-modal peripheral physiological signals

Gohumpu,

Xue,

Bao

2023

Front. Comput. Sci.

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IntroductionHealthcare wearables allow researchers to develop various system approaches that recognize and understand the human emotional experience. Previous research has indicated that machine learning classifiers, such as Support Vector Machine (SVM), K-Nearest Neighbors (KNN), and Decision Tree (DT), can improve the accuracy of physiological signal analysis and emotion recognition. However, various emotions can have distinct effects on physiological signal alterations. Therefore, solely relying on a single type of physiological signal analysis is insufficient for accurately recognizing and understanding human emotional experiences.MethodsResearch on multi-modal emotion recognition systems (ERS) has commonly gathered physiological signals using expensive devices, which required participants to remain in fixed positions in the lab setting. This limitation restricts the potential for generalizing the ERS technology for peripheral use in daily life. Therefore, considering the convenience of data collection from everyday devices, we propose a multi-modal physiological signals-based ERS based on peripheral signals, utilizing the DEAP database. The physiological signals selected for analysis include photoplethysmography (PPG), galvanic skin response (GSR), and skin temperature (SKT). Signal features were extracted using the “Toolbox for Emotional Feature Extraction from Physiological Signals” (TEAP) library and further analyzed with three classifiers: SVM, KNN, and DT.ResultsThe results showed improved accuracy in the proposed system compared to a single-modal ERS application, which also outperformed current DEAP multi-modal ERS applications.DiscussionThis study sheds light on the potential of combining multi-modal peripheral physiological signals in ERS for ubiquitous applications in daily life, conveniently captured using smart devices.

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“…Welch's method is one of the most common approaches to calculate PSD [49]. PSD is often used in the studies that include frequency domain HRV features for stress detection such as total HF or LF power [66], [67], [68], [86], [114], [114], [115], [116], [117], [118], [119], [120], [121].…”

Section: Early Modality Fusion (N=1)mentioning

confidence: 99%

Machine Learning, Deep Learning and Data Preprocessing Techniques for Detection, Prediction, and Monitoring of Stress and Stress-related Mental Disorders: A Scoping Review (Preprint)

Razavi,

Ziyadidegan,

Jahromi

et al. 2023

JMIR Mental Health

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Background: Mental stress and its consequent mental disorders (MDs) constitute a significant public health issue. With the advent of machine learning (ML), there's potential to harness computational techniques for better understanding and addressing mental stress and MDs. This comprehensive review seeks to elucidate the current ML methodologies employed in this domain to pave the way for enhanced detection, prediction, and analysis of mental stress and its subsequent mental disorders.Objective: This review aims to investigate the scope of Machine Learning (ML) methodologies employed in the detection, prediction, and analysis of mental stress and its consequent mental disorders (MDs).Methods: Utilizing a rigorous scoping review process with Preferred Reporting Items for Systematic Reviews and Meta-Analyses extention for Scoping Reviews (PRISMA-ScR) guidelines, this investigation delves into the latest ML algorithms, preprocessing techniques, and data types employed in the context of stress and stress-related MDs. Results:The findings highlight that Support Vector Machine (SVM), Neural Network (NN), and Random Forest (RF) models consistently exhibit superior accuracy and robustness among all machine learning algorithms examined. Physiological parameters such as heart rate measurements and skin response are prevalently used as stress predictors due to their rich explanatory information concerning stress and stress-related MDs, as well as the relative ease of data acquisition. The application of dimensionality reduction techniques, including mappings, feature selection, filtering, and noise reduction, is frequently observed as a crucial step preceding the training of ML algorithms. Conclusions:The synthesis of this review identifies significant research gaps and outlines future directions for the field. These encompass areas such as model interpretability, model personalization, the incorporation of naturalistic settings, and real-time processing capabilities for the detection and prediction of stress and stress-related MDs.

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Heart Rate Variability and Electrodermal Activity Biosignal Processing: Predicting the Autonomous Nervous System Response in Mental Stress

Cited by 7 publications

References 39 publications

Biomarkers of Anxiety and Depression – A Novel Method of Tracking the Autonomic Nervous System with Machine Learning

Biomarkers of Anxiety and Depression – A Novel Method of Tracking the Autonomic Nervous System with Machine Learning

Emotion recognition with multi-modal peripheral physiological signals

Machine Learning, Deep Learning and Data Preprocessing Techniques for Detection, Prediction, and Monitoring of Stress and Stress-related Mental Disorders: A Scoping Review (Preprint)

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