Intelligent depression detection based on multi-physiological signals acquired by wearable devices
10.3969/j.issn.1005-202X.2025.09.010
- VernacularTitle:基于穿戴式多生理信号的抑郁智能检测
- Author:
Keming CAO
1
;
Lulu ZHAO
1
;
Minghui ZHAO
1
;
Zining WANG
1
;
Jianqing LI
1
;
Chengyu LIU
1
Author Information
1. 东南大学仪器科学与工程学院/数字医学工程全国重点实验室,江苏 南京 210096
- Publication Type:Journal Article
- Keywords:
wearable device;
multimodal;
artificial intelligence;
depression recognition
- From:
Chinese Journal of Medical Physics
2025;42(9):1191-1196
- CountryChina
- Language:Chinese
-
Abstract:
Depression,as a severe psychological and psychiatric disorder,significantly impairs the long-term physical and mental health of patients.Current depression detection methods are plagued by strong subjectivity,limited techniques,and inadequate intelligence.Previous studies have mostly relied on single-modal signal analysis,making it difficult to comprehensively reflect the multidimensional characteristics of depression.Based on the independently developed intelligent depression detection system,wearable devices are used to collect prefrontal dual-lead EEG signals,PPG signals,and single-lead ECG signals.Data from 30 patients with depression and 40 healthy controls are collected and analyzed.A multimodal depression recognition model named RBLF-Net is proposed,which integrates spatiotemporal features,weighted attention,and random forests to utilize the multi-signal features for depression recognition.The model exhibits superior performance in the five-fold cross-validation,achieving a classification accuracy of 81.43%,a precision of 81.02%,and a recall rate of 81.25%,outperforming other comparative models,and thus providing an intelligent analysis approach for depression recognition from the perspective of multi-modal fusion.
