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OA

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.