ArticleTechnology and health care : official journal of the European Society for Engineering and Medicine2019
Entropy analysis of heart rate variability and its application to recognize major depressive disorder: A pilot study.
Article in Technology and health care : official journal of the European Society for Engineering and Medicine, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 33 papers, 2 of them syntheses that pooled it.
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Who cites it
33 citing papers in PubMed, 2 syntheses or guidelines pooled it, 74 citations in OpenAlex.
- Effects of sleep deprivation on heart rate variability: a systematic review and meta-analysis.Frontiers in neurology · 2025Pooled it
- Digital phenotype of mood disorders: A conceptual and critical review.Frontiers in psychiatry · 2022Pooled it
- Characterizing infant leg movements using 72-h wearable sensor data: Descriptive analysis from a large, heterogenous sample of infants 0-2 months of age from the HEALthy Brain and Child Development Study.Infant behavior & development · 2026Article
- Impact of positive end-expiratory pressure on autonomic nervous system activity and its interaction with cerebrovascular reactivity - an experimental study.Journal of clinical monitoring and computing · 2026Article
- Enhancing Physiological Complexity Through Mindfulness: A Wearable-Based Intervention for First Responders and Their Partners.Healthcare (Basel, Switzerland) · 2026Article
- Complexity Analysis of Skin Nerve Activity for Quantitative Assessment of Acute Sympathetic Nervous System Activation.Sensors (Basel, Switzerland) · 2026Article
- Entropy of Muscle Fiber Histology Predicts Mobility in Older Adults: The Study of Muscle, Mobility, and Aging.Aging cell · 2026Article
- Artificial Intelligence-Based Depression Detection.Sensors (Basel, Switzerland) · 2026Article
- Diagnostic Entropy as an Administrative Measure of Cross-System Diagnostic Dispersion in Noncommunicable Disease Inpatients: A Methodological Validation Study.Journal of nursing management · 2026Article
- Heart Rate Variability Patterns Reflect Yoga Intervention in Chronically Stressed Pregnant Women: A Quasi-Randomized Controlled Trial.Bioengineering (Basel, Switzerland) · 2025Article
- Electromyographic Diaphragm and Electrocardiographic Signal Analysis for Weaning Outcome Classification in Mechanically Ventilated Patients.Sensors (Basel, Switzerland) · 2025Article
- Optimizing Mental Stress Detection via Heart Rate Variability Feature Selection.Sensors (Basel, Switzerland) · 2025Article
- Deep learning-based stress detection from RR intervals in major depressive disorder, panic disorder, and healthy individuals.Frontiers in psychiatry · 2025Article
- Article
- Exploring the Hidden Complexity: Entropy Analysis in Pulse Oximetry of Female Athletes.Biosensors · 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
- What can entropy metrics tell us about the characteristics of ocular fixation trajectories?PloS one · 2024Article
- Automatic Depression Detection Using Smartphone-Based Text-Dependent Speech Signals: Deep Convolutional Neural Network Approach.Journal of medical Internet research · 2023Article
- When Heart Beats Differently in Depression: Review of Nonlinear Heart Rate Variability Measures.JMIR mental health · 2023Review
- Heart Rate Complexity and Autonomic Modulation Are Associated with Psychological Response Inhibition in Healthy Subjects.Entropy (Basel, Switzerland) · 2023Article
Corrections and comments
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Authors and funding
7 authors at 4 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThe current method to evaluate major depressive disorder (MDD) relies on subjective clinical interviews and self-questionnaires.
objectiveAutonomic imbalance in MDD patients is characterized using entropy measures of heart rate variability (HRV). A machine learning approach for screening depression based on the entropy is demonstrated.
methodsThe participants experience five experimental phases: baseline (BASE), stress task (MAT), stress task recovery (REC1), relaxation task (RLX), and relaxation task recovery (REC2). The four entropy indices, approximate entropy, sample entropy, fuzzy entropy, and Shannon entropy, are extracted for each phase, and a total of 20 features are used. A support vector machine classifier and recursive feature elimination are employed for classification.
resultsThe entropy features are lower in the MDD group; however, the disease does not have a significant effect. Experimental tasks significantly affect the features. The entropy did not recover during REC1. The differences in the entropy features between the two groups increased after MAT and showed the largest gap in REC2. We achieved 70% accuracy, 64% sensitivity, and 76% specificity with three optimal features during RLX and REC2.
conclusionMonitoring of HRV complexity changes when a subject experiences autonomic arousal and recovery can potentially facilitate objective depression recognition.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.