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Automated detection of panic disorder based on multimodal physiological signals using machine learning

  • Eun Hye Jang (Welfare and Medical ICT Research Department, Electronics and Telecommunications Research Institute) ;
  • Kwan Woo Choi (Department of Psychiatry, Korea University) ;
  • Ah Young Kim (Welfare and Medical ICT Research Department, Electronics and Telecommunications Research Institute) ;
  • Han Young Yu (Industry and IoT Intelligence Research Department, Electronics and Telecommunications Research Institute) ;
  • Hong Jin Jeon (Department of Psychiatry in the Samsung Medical Center, Sungkyunkwan University School of Medicine) ;
  • Sangwon Byun (Department of Electronics Engineering, Incheon National University)
  • Received : 2021.08.30
  • Accepted : 2022.05.02
  • Published : 2023.02.20
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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.

Keywords

Acknowledgement

This work was supported by the Electronics and Telecommunications Research Institute (ETRI)'s internal funds, Development of Digital Biopsy Core Technology for high-precision Diagnosis and Therapy of Senile Disease (21YR2500), Institute for Information & Communications Technology Promotion (IITP) grant funded by the Korea government (MSIT) (No. 2015-0-00062), and National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (No. 2020R1F1A1049236).

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