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Entropy analysis of heart rate variability and its application to recognize major depressive disorder: A pilot study.

ABSTRACT: BACKGROUND:The current method to evaluate major depressive disorder (MDD) relies on subjective clinical interviews and self-questionnaires. OBJECTIVE:Autonomic 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. METHODS:The 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. RESULTS:The 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. CONCLUSION:Monitoring of HRV complexity changes when a subject experiences autonomic arousal and recovery can potentially facilitate objective depression recognition.

SUBMITTER: Byun S

PROVIDER: S-EPMC6597986 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature

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Entropy analysis of heart rate variability and its application to recognize major depressive disorder: A pilot study.

Byun Sangwon S Kim Ah Young AY Jang Eun Hye EH Kim Seunghwan S Choi Kwan Woo KW Yu Han Young HY Jeon Hong Jin HJ

Technology and health care : official journal of the European Society for Engineering and Medicine 20190101 S1

<h4>Background</h4>The current method to evaluate major depressive disorder (MDD) relies on subjective clinical interviews and self-questionnaires.<h4>Objective</h4>Autonomic 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.<h4>Methods</h4>The participants experience five experimental phases: baseline (BASE), stress task (MAT), stress task recovery (REC1), r ...[more]

PMID: 31045557

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Project description:Background: Major Depressive Disorder (MDD) is a moderately heritable disorder with a high lifetime prevalence. At present, laboratory blood tests to support MDD diagnosis are not available. Methods: We used a classifier approach on blood gene expression profiles of a unique set of non-medicated subjects (MDD patients and controls) to select genes of which expression is predictive for disease status. To reveal blood gene expression changes related to MDD disease, we applied a powerful ex vivo stimulus to the blood, i.e. incubation with lipopolysaccharide (LPS; 10 ng/ml blood). Results: Based on LPS-stimulated blood gene expression using whole-genome microarrays in 42 subjects (primary cohort; 21 MDD patients (mean age 42.3 years), 21 healthy controls (mean age 41.9 years)), we identified a set of genes (CAPRIN1, CLEC4A, KRT23, MLC1, PLSCR1, PROK2, ZBTB16) that serves as a molecular signature of MDD. These findings were validated for the primary cohort using an independent quantitative PCR method (P = 0.007). The difference between depressive patients and controls was confirmed (P = 0.019) in a replication cohort of 13 patients with MDD (mean age 42.8 years) and 14 controls (mean age 45.6 years). The MDD-signature score comprised of expression levels of 7 genes could discriminate depressive patients from controls with sensitivity of 76.9% and specificity of 71.8%. Conclusions: We show for the first time that molecular analysis of stimulated blood cells can be used as an endophenotype for MDD diagnosis, which is a milestone in establishing biomarkers for neuropsychiatric disorders with moderate heritability in general. Our results may provide a new entry point for following and predicting treatment outcome, as well as prediction of severity and recurrence of MDD. In total, 33 MDD patients and 34 healthy controls were analyzed using basal gene expression in whole blood, and gene expression from whole blood that was stimulated with LPS for 5-6 h, using microarrays. Patients were arbitrarily selected from all patients to serve as primary cohort (nMDD = 21 (MDD01-MDD21); nControls = 21 (Con01-Con21)), or replication cohort (nMDD = 12 (MDD22-MDD35); nControls = 13 (Con22-Con37)) using microarrays. This submission does not include Samples CON21_LPS or CON30_LPS.

Dataset Information

Entropy analysis of heart rate variability and its application to recognize major depressive disorder: A pilot study.

Publications

Entropy analysis of heart rate variability and its application to recognize major depressive disorder: A pilot study.

Similar Datasets

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