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Cited 1 time in webofscience Cited 2 time in scopus
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Automated detection of panic disorder based on multimodal physiological signals using machine learningopen access

Authors
Jang, Eun HyeChoi, Kwan WooKim, Ah YoungYu, Han YoungJeon, Hong JinByun, 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.
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