MM-SleepNet: An Automatic Sleep Staging Network Based on Modal Re-Weighting and Mamba

This is the authors' abstract. We don't add key points for this paper.

Sleep staging is the key step in sleep quality assessment and sleep disorder diagnosis. In clinical practice, doctors usually use polysomnography (PSG) to stage sleep. PSG contains physiological signals such as electroencephalography (EEG) and electrooculography (EOG) and is divided into continuous epochs of fixed duration. In order to achieve more efficient and accurate automatic interpretation of PSG, this paper proposes MM-SleepNet, an automatic sleep staging network based on modal re-weighting and Mamba. MM-SleepNet divides each epoch into several overlapping patches and uses a newly designed patch feature extractor to extract the features of each patch, in order to solve the problem of key waveforms in the patch being obscured by non-key waveforms throughout the epoch. Afterward, MM-SleepNet calculates the importance of different signals on patches at the same time point through a dynamic re-weighting module. MM-SleepNet uses a bidirectional Mamba encoder that considers positional encoding to learn contextual information across epochs to adapt to the transition patterns between sleep stages. The experimental results on the Sleep-EDF and ISRUC datasets show that the proposed MM-SleepNet outperforms state-of-the-art methods. In addition, ablation experiments have demonstrated the effectiveness of various modules in MM-SleepNet.