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Journal Article
Detection of Major Depressive Disorder from Linear and Nonlinear Heart Rate Variability Features during Mental Task Protocol
- Authors
- Sangwon Byun, Ah Young Kim, Eun Hye Jang, Seunghwan Kim, Kwan Woo Choi, Han Young Yu, Hong Jin Jeon
- Issue Date
- 2019-09
- Citation
- Computers in Biology and Medicine, v.112, pp.1-13
- ISSN
- 0010-4825
- Publisher
- Elsevier
- Language
- English
- Type
- Journal Article
- Project Code
- 17HS5600, The development of skin adhesive patches for the monitoring and prediction of mental disorders, Seunghwan Kim
- Abstract
- Background: Major depressive disorder (MDD) is one of the leading causes of disability; however, current MDD diagnosis methods lack an objective assessment of depressive symptoms. Here, a machine learning approach to separate MDD patients from healthy controls was developed based on linear and nonlinear heart rate variability (HRV), which reflects the autonomic cardiovascular regulation. Methods: HRV data were collected from 37 MDD patients and 41 healthy controls during five 5-min experimental phases: the baseline, a mental stress task, stress recovery, a relaxation task, and relaxation task recovery. The experimental protocol was designed to assess the autonomic responses to stress and recovery. Twenty HRV indices were extracted from each phase, and a total of 100 features were used for classification using a support vector machine (SVM). SVM-recursive feature elimination (RFE) and statistical filter were employed to perform feature selection. Results: We achieved 74.4% accuracy, 73% sensitivity, and 75.6% specificity with two optimal features selected by SVM-RFE, which were extracted from the stress task recovery and mental stress phases. Classification performance worsened when individual phases were used separately as input data, compared to when all phases were included. The SVM-RFE using nonlinear and Poincar챕 plot HRV features performed better than that using the linear indices and matched the best performance achieved by using all features. Conclusions: We demonstrated the machine learning-based diagnosis of MDD using HRV analysis. Monitoring the changes in linear and nonlinear HRV features for various autonomic nervous system states can facilitate the more objective identification of MDD patients.
- KSP Keywords
- Autonomic nervous system(ANS), Best performance, Cardiovascular regulation, Classification Performance, Depressive symptoms, Feature selection(FS), HRV analysis, HRV features, Healthy controls, Heart rate variability, Learning-based
This work is distributed under the term of Creative Commons License (CCL)
(CC BY NC ND)
(CC BY NC ND)
