AUTOMATIC SLEEP STAGE CLASSIFICATION USING MULTI-PHYSIOLOGICAL SIGNALS BASED ON PCBSLEEPNET
Keywords:
Deep learning, Sleep staging, Multi-physiological signals, PolysomnographyAbstract
Automatic sleep staging, a key technology in sleep medicine, faces feature selection challenges due to multi-modal physiological signal heterogeneity. This study establishes a framework to evaluate bioelectrical signals' impacts on deep neural network feature representation, aiming to identify optimal integration paradigms.We propose a multi-branch hybrid neural network with: 1) a three-channel parallel convolutional module incorporating cascaded residual and dilated convolutions for time-frequency feature extraction, and 2) bidirectional long short-term memory (BiLSTM) layers for temporal dependency modeling. We systematically analyzed multi-channel electroencephalogram (EEG), single-channel physiological signals, and their combinations covering both single-channel and multi-channel configurations of EEG, electrooculogram (EOG), and electromyogram (EMG). Results demonstrate that six-channel EEG configurations outperformed single/dual-channel implementations. In the single- and dual-channel EEG settings, the C3-M2 and C4-M1 + C3-M2 channels exhibited superior performance. EEG maintained superior classification capability over EOG/EMG. The tri-modal fusion (EEG+EOG+EMG) achieved peak performance (accuracy: 89.5%, Cohen’s κ: 0.85). This demonstrates EEG's central role in sleep staging, while EOG/EMG synergistically enhance model performance. The findings reveal heterogeneous bioelectric signal interactions in deep neural networks, providing foundations for optimizing signal fusion strategies and model architectures.References
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