Subject-Adaptation Salient Wave Detection Network for Multimodal Sleep Stage Classification

Sleep stage classification is an important step in the diagnosis and treatment of sleep disorders. Despite the high classification performance of previous sleep stage classification work, some challenges remain unresolved: (1) How to effectively capture salient waves in sleep signals to improve sleep stage classification results. (2) How to capture salient waves affected by inter-subject variability. (3) How to adaptively regulate the importance of different modals for different sleep stages. To address these challenges, we propose SleepWaveNet, a multimodal salient wave detection network, which is motivated by the salient object detection task in computer vision. It has a U-Transformer structure to detect salient waves in sleep signals. Meanwhile, the subject-adaptation wave extraction architecture based on transfer learning can adapt to the information of target individuals and extract salient waves with inter-subject variability. In addition, the multimodal attention module can adaptively enhance the importance of specific modal data for sleep stage classification tasks. Experiments on three datasets show that SleepWaveNet has better overall performance than existing baselines. Moreover, visualization experiments show that the model has the ability to capture salient waves with inter-subject variability.