Automated detection of panic disorder based on multimodal physiological signals using machine learningopen access
- Authors
- Jang, Eun Hye; Choi, Kwan Woo; Kim, Ah Young; Yu, Han Young; Jeon, Hong Jin; Byun, Sangwon
- Issue Date
- Feb-2023
- Publisher
- 한국전자통신연구원
- Keywords
- anxiety disorder; autonomic nervous system (ANS) response; deep learning; electrocardiogram (ECG); heart rate variability (HRV); machine learning; mental stress task; multimodal; panic disorder; physiological signals
- Citation
- ETRI Journal, v.45, no.1, pp 105 - 118
- Pages
- 14
- Indexed
- SCIE
SCOPUS
KCI
- Journal Title
- ETRI Journal
- Volume
- 45
- Number
- 1
- Start Page
- 105
- End Page
- 118
- URI
- https://scholarworks.korea.ac.kr/kumedicine/handle/2021.sw.kumedicine/61393
- DOI
- 10.4218/etrij.2021-0299
- ISSN
- 1225-6463
2233-7326
- Abstract
- We tested the feasibility of automated discrimination of patients with panic disorder (PD) from healthy controls (HCs) based on multimodal physiological responses using machine learning. Electrocardiogram (ECG), electrodermal activity (EDA), respiration (RESP), and peripheral temperature (PT) of the participants were measured during three experimental phases: rest, stress, and recovery. Eleven physiological features were extracted from each phase and used as input data. Logistic regression (LoR), k-nearest neighbor (KNN), support vector machine (SVM), random forest (RF), and multilayer perceptron (MLP) algorithms were implemented with nested cross-validation. Linear regression analysis showed that ECG and PT features obtained in the stress and recovery phases were significant predictors of PD. We achieved the highest accuracy (75.61%) with MLP using all 33 features. With the exception of MLP, applying the significant predictors led to a higher accuracy than using 24 ECG features. These results suggest that combining multimodal physiological signals measured during various states of autonomic arousal has the potential to differentiate patients with PD from HCs.
- Files in This Item
- There are no files associated with this item.
- Appears in
Collections - 2. Clinical Science > Department of Psychiatry > 1. Journal Articles
Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.