MFCSNet: Multi-Modal Frequency Coupling Sequence Network for Automatic Sleep Staging

Sleep staging is essential for assessing sleep quality and diagnosing disorders. Current deep learning models exhibit limitations in spatial feature modeling, cross-frequency coupling awareness, contextual modeling, and stage transition dynamics. We propose a novel deep neural architecture integrating multi-band and multimodal modeling. It includes Frequency-Time-Spatial Convolution (FTSC) with wavelet decompositions for multi-scale frequency features, $1 \times 1$ convolutions for crossfrequency coupling, and spatiotemporal convolutions for local patterns. Enhanced by Multimodal Bottleneck Transformer (MBT) and Epoch Transformer (ET) for cross-modal semantics and a Conditional Random Field (CRF) for physiologically plausible transitions, our approach demonstrates state-of-the-art performance and robustness in experiments on multiple public datasets.