  Monitoring sleep states is essential for evaluating sleep quality and
diagnosing sleep disorders. Traditional manual staging is time-consuming and
prone to subjective bias, often resulting in inconsistent outcomes. Here, we
developed an automated model for sleep staging and disorder classification to
enhance diagnostic accuracy and efficiency. Considering the characteristics of
polysomnography (PSG) multi-lead sleep monitoring, we designed a multimodal
sleep state classification model, MSSC-BiMamba, that combines an Efficient
Channel Attention (ECA) mechanism with a Bidirectional State Space Model
(BSSM). The ECA module allows for weighting data from different sensor
channels, thereby amplifying the influence of diverse sensor inputs.
Additionally, the implementation of bidirectional Mamba (BiMamba) enables the
model to effectively capture the multidimensional features and long-range
dependencies of PSG data. The developed model demonstrated impressive
performance on sleep stage classification tasks on both the ISRUC-S3 and
ISRUC-S1 datasets, respectively containing data with healthy and unhealthy
sleep patterns. Also, the model exhibited a high accuracy for sleep health
prediction when evaluated on a combined dataset consisting of ISRUC and
Sleep-EDF. Our model, which can effectively handle diverse sleep conditions, is
the first to apply BiMamba to sleep staging with multimodal PSG data, showing
substantial gains in computational and memory efficiency over traditional
Transformer-style models. This method enhances sleep health management by
making monitoring more accessible and extending advanced healthcare through
innovative technology.
