数据采集与处理2026，Vol.41Issue(4)：966-980,15.DOI:10.16337/j.1004-9037.2026.04.004
基于便携式脑电和心电的双层级融合抑郁症智能检测方法
A Hierarchical Fusion Method for Intelligent Depression Detection Based on Porta-ble EEG and ECG
摘要
Abstract
Major depressive disorder(MDD)is a prevalent psychiatric disorder.Conventional screening and diagnosis mainly rely on structured interviews and rating scales,whose subjectivity and limited accessibility hinder early identification in primary care and community settings.To address this problem,this study develops and validates P-MFCNet,a portable multimodal framework for depression recognition based on single-channel electroencephalography(EEG)and wearable electrocardiography(ECG).A multi-task protocol comprising eyes-open rest,negative emotion elicitation,eyes-closed rest,and a semi-structured clinical interview was designed,and synchronized multimodal data were collected at two medical centers.P-MFCNet employs a hierarchical fusion strategy based on Transformer encoders and classification tokens(CLS)to model complementary information across modalities and differences among task states.Modality-level and task-level representations are learned separately and integrated through hierarchical CLS aggregation to generate a subject-level representation for MDD-versus-HC classification using a ResNet head.In single-center five-fold cross-validation,P-MFCNet achieves an accuracy of 0.842 4 and an AUC of 0.796 7.In cross-center external testing,it achieves an accuracy of 0.810 0 and an AUC of 0.875 0.The model outperforms unimodal EEG and ECG models in accuracy,sensitivity,and overall performance,while maintaining a favorable balance between sensitivity and specificity compared with baseline methods.Post-hoc analyses using zero-out ablation and attention visualization show that ECG/HRV features provide discriminative evidence,whereas EEG contributes complementary information under negative emotion elicitation and resting conditions.These findings demonstrate the feasibility of portable EEG-ECG fusion for depression screening and indicate cross-center generalizability and interpretability.关键词
重度抑郁障碍/便携式脑电/可穿戴心电/多模态融合Key words
major depressive disorder(MDD)/portable EEG/wearable ECG/multimodal fusion分类
信息技术与安全科学引用本文复制引用
应仡扬,裘生梁,陈熙昂,江海腾..基于便携式脑电和心电的双层级融合抑郁症智能检测方法[J].数据采集与处理,2026,41(4):966-980,15.基金项目
脑科学与类脑研究国家科技重大专项(2022ZD0212400) （2022ZD0212400）
大学生创新创业训练项目(202510344031). Brain Science and Brain-like Intelligence Technology—National Science and Technology Major Project(No.2022ZD0212400) （202510344031）
College Students'Innovative Entrepreneurial Training Plan Program(No.202510344031). （No.202510344031）
