MSSC-BiMamba: Multimodal Sleep Stage Classification with Bidirectional Mamba
Keywords: sleep stage classification, Mamba, efficient channel attention, bidirectional state space model
TL;DR: An automated multimodal model, MSSC-BiMamba, integrates efficient channel attention and bidirectional state space modeling to achieve accurate sleep staging from polysomnography data.
Abstract: 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 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 multi-dimensional features and long-range dependencies of PSG data. The developed model demonstrated impressive performance on sleep stage classification tasks on the ISRUC-S3 and ISRUC-S1 datasets, respectively, including healthy and unhealthy sleep patterns. 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.
Primary Area: learning on time series and dynamical systems
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Submission Number: 8840
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