The Capacity of Skin Potential in Generalized Anxiety Disorder Discrimination Using Weighted Feature Fusion

Abstract

Background: In today's high-pressure society, anxiety disorders have come to the forefront of public attention. Distinguishing anxiety disorders accurately, conveniently, and effectively is of immense social value and scientific importance.Methods: Thirty-one generalized anxiety disorder (GAD) patients and twenty healthy controls (HC) were recruited in this study, and skin potential (SP) signals were collected during the resting state and task state using a single-channel portable device. Time-domain, spectral-domain, and nonlinear features were extracted from these two states, and feature coefficients were obtained by examining the relationship between individual features and clinical scales. Furthermore, single-mode features (resting state or task state), bimodal features (both two states with direct feature combination) and fused bimodal features (both two states with weighted feature coefficients) were fed into multiple machine learning algorithms including support vector machine (SVM), decision trees (DT), random forest (RF), and cat boost (CB) to distinguish between these two groups at the individual level. Model performances were systematically evaluated and compared using 10 fold cross-validation.Results: The weighted feature fusion approach achieved the best performance in individual GAD prediction (88.31%), comparing to single-mode features (resting state: 72.68%; task state: 79.07%) and direct feature combinations (84.57%). Conclusion:  Our proposed weighted feature fusions from resting state and task state provides a promising tool to aid in early screening and clinical support in the GAD diagnosis.

Note:
Funding Declaration: This work was supported by STI2030-Major Projects (grant number 2022ZD0212400 ), National Natural Science Foundation of China (grant number 82371453), Nonprofit Central Research Institute Fund of Chinese Academy of Medical Sciences （ grant number 2023-PT310-01）and Hangzhou Biomedical and Health Industry Special Projects for Science and Technology (grant number 2021WJCY240 ).

Conflicts of Interest: None.

Ethical Approval: The study was approved by the Ethics Committee of Hangzhou Seventh People's Hospital and written informed consent was obtained from all participants.

Keywords: Generalized anxiety disorders, Skin potential, Weighted feature fusion, Machine learning