ArticleScientific reports2018
Automatic detection of major depressive disorder using electrodermal activity.
Article in Scientific reports, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers, 3 of them syntheses that pooled it.
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Who cites it
28 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Machine Learning Techniques for Arousal Classification from Electrodermal Activity: A Systematic Review.Sensors (Basel, Switzerland) · 2022Pooled it
- Digital phenotype of mood disorders: A conceptual and critical review.Frontiers in psychiatry · 2022Pooled it
- Shared and distinct brain fMRI response during performance of working memory tasks in adult patients with schizophrenia and major depressive disorder.Human brain mapping · 2021Pooled it
- Elevated Galvanic Skin Response and Associated Brain Network Patterns in Youth at High Familial Risk for Bipolar Disorder.JAACAP open · 2026Article
- The interaction between the immune system and the autonomic nervous system in the pathophysiology of major depressive disorder: a scoping review.Brain, behavior, & immunity - health · 2026Review
- Effects of Dante's Poetry on Emotional Arousal of Healthy Subjects and Patients with Spinal Cord Injury: A Pilot Study.Behavioral sciences (Basel, Switzerland) · 2026Article
- Clinical use of electrodermal activity and current developments.European biophysics journal : EBJ · 2026Review
- Artificial Intelligence-Based Depression Detection.Sensors (Basel, Switzerland) · 2026Article
- Case Report: Integrating CBT, hypnosis-based consciousness activation techniques, and yoga-based postural training: a three-pillar approach used for migrant populations.Frontiers in psychiatry · 2026Article
- Enhancing Mindfulness-Based Cognitive Therapy in a Virtual Reality: A Prospective Interventional Study.Scientific reports · 2025Article
- Evidence of differences in diurnal electrodermal, temperature and heart rate patterns by mental health status in free-living data.BMJ mental health · 2025Article
- Integration of Multi-Modal Biosensing Approaches for Depression: Current Status, Challenges, and Future Perspectives.Sensors (Basel, Switzerland) · 2025Review
- Contribution of physiological dynamics in predicting major depressive disorder severity.Psychophysiology · 2025Article
- Could electrodermal activity detect emotions soon?Journal of electrical bioimpedance · 2025Article
- Effect of Acute Ketamine Treatment on Sympathetic Regulation Indexed by Electrodermal Activity in Adolescent Major Depression.Pharmaceuticals (Basel, Switzerland) · 2024Article
- Task-state skin potential abnormalities can distinguish major depressive disorder and bipolar depression from healthy controls.Translational psychiatry · 2024Article
- Automated classification of stress and relaxation responses in major depressive disorder, panic disorder, and healthy participants via heart rate variability.Frontiers in psychiatry · 2024Article
- Exploring Digital Biomarkers of Illness Activity in Mood Episodes: Hypotheses Generating and Model Development Study.JMIR mHealth and uHealth · 2023Observational
- Automatic Depression Detection Using Smartphone-Based Text-Dependent Speech Signals: Deep Convolutional Neural Network Approach.Journal of medical Internet research · 2023Article
- Digital Phenotyping for Differential Diagnosis of Major Depressive Episode: Narrative Review.JMIR mental health · 2023Review
Corrections and comments
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Major depressive disorder (MDD) is a common psychiatric disorder and the leading cause of disability worldwide. However, current methods used to diagnose depression mainly rely on clinical interviews and self-reported scales of depressive symptoms, which lack objectivity and efficiency. To address this challenge, we present a machine learning approach to screen for MDD using electrodermal activity (EDA). Participants included 30 patients with MDD and 37 healthy controls. Their EDA was measured during five experimental phases consisted of baseline, mental arithmetic task, recovery from the stress task, relaxation task, and recovery from the relaxation task, which elicited multiple alterations in autonomic activity. Selected EDA features were extracted from each phase, and differential EDA features between two distinct phases were evaluated. By using these features as input data and performing feature selection with SVM-RFE, 74% accuracy, 74% sensitivity, and 71% specificity could be achieved by our decision tree classifier. The most relevant features selected by SVM-RFE included differential EDA features and features from the stress and relaxation tasks. These findings suggest that automatic detection of depression based on EDA features is feasible and that monitoring changes in physiological signal when a subject is experiencing autonomic arousal and recovery may enhance discrimination power.
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