STCN: A Lightweight Sleep Staging Model with Multiple Channels

Sleep staging is an important means of diagnosing sleep disorders and monitoring sleep quality. Concurrently, the RNN (Recurrent Neural Network) models commonly used in this field limits the overall calculation efficiency and performance. Specifically, we utilize the TCN (Temporal Convolutional Network) model advanced in the audio field as an alternative for RNN models to improve temporal information collection capability. Leverage the SE (squeeze-and-extraction) module to merge the multi-lead information flexibly. Implement the feature extraction layer by the design of CNN to reduce computational complexity. The proposed model, named STCN, achieved 85.01% accuracy, 78.80% F1 value on the Sleep-EDFx dataset, and achieved 84.10% accuracy, 81.27% F1 value on the Physionet 2018 dataset. Only 1.6 million parameters are obtained in STCN, which is significantly smaller than the parameters of Deepsleepnet. The proposed model is compatible with scenarios from different datasets, achieves an adaptive fusion of each lead information and supplies a more reliable prediction for sleep quality assessment to provide a foundation for convenient applications in the future.