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Journal Article
Automated detection of panic disorder based on multimodal physiological signals using machine learning
- Authors
- Eun Hye Jang, Kwan Woo Choi, Ah Young Kim, Han Young Yu, Hong Jin Jeon, Sangwon Byun
- Issue Date
- 2023-02
- Citation
- ETRI Journal, v.45, no.1, pp.105-118
- ISSN
- 1225-6463
- Publisher
- 한국전자통신연구원 (ETRI)
- Language
- English
- Type
- Journal Article
- 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.
- KSP Keywords
- 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)
This work is distributed under the term of Korea Open Government License (KOGL)
(Type 4: : Type 1 + Commercial Use Prohibition+Change Prohibition)
(Type 4: : Type 1 + Commercial Use Prohibition+Change Prohibition)
