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학술지 Automated detection of panic disorder based on multimodal physiological signals using machine learning
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저자
장은혜, 최관우, 김아영, 유한영, 전홍진, 변상원
발행일
202302
출처
ETRI Journal, v.45 no.1, pp.105-118
ISSN
1225-6463
출판사
한국전자통신연구원 (ETRI)
DOI
https://dx.doi.org/10.4218/etrij.2021-0299
협약과제
17HS5600, 정신 질환의 모니터링 및 징후 예측을 위한 피부 부착형 센서 모듈 개발, 김승환
초록
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.
KSP 제안 키워드
Automated Detection, Cross validation(CV), ECG features, Electrodermal activity, Healthy controls, Linear regression analysis, Logistic Regression(LR), Physiological features, Physiological responses, Random forest (rf), Support VectorMachine(SVM)
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