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A temporal multi-scale hybrid attention network for sleep stage classification

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Abstract

Sleep is crucial for human health. Automatic sleep stage classification based on polysomnogram (PSG) is meaningful for the diagnosis of sleep disorders, which has attracted extensive attention in recent years. Most existing methods could not fully consider the different transitions of sleep stages and fit the visual inspection of sleep experts simultaneously. To this end, we propose a temporal multi-scale hybrid attention network, namely TMHAN, to automatically achieve sleep staging. The temporal multi-scale mechanism incorporates short-term abrupt and long-term periodic transitions of the successive PSG epochs. Furthermore, the hybrid attention mechanism includes 1-D local attention, 2-D global attention, and 2-D contextual sparse multi-head self-attention for three kinds of sequence-level representations. The concatenated representation is subsequently fed into a softmax layer to train an end-to-end model. Experimental results on two benchmark sleep datasets show that TMHAN obtains the best performance compared with several baselines, demonstrating the effectiveness of our model. In general, our work not only provides good classification performance, but also fits the actual sleep staging processes, which makes contribution for the combination of deep learning and sleep medicine.

The brief overall architecture of temporal multi-scale hybrid attention network for sleep stage classification.

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Funding

This work is supported in part by the Natural Science Foundation of Beijing Municipality under Grant No. 4212001.

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Authors and Affiliations

  1. Beijing Key Laboratory of Computational Intelligence and Intelligent System, Faculty of Information Technology, Beijing University of Technology, Beijing, 100124, China

    Zheng Jin & Kebin Jia

  2. Beijing Laboratory of Advanced Information Networks, Beijing, 100124, China

    Zheng Jin & Kebin Jia

Authors
  1. Zheng Jin
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  2. Kebin Jia
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Correspondence to Kebin Jia.

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All the biomedical sleep datasets used in this work are downloaded from the well-known open-source website PhysioNet (https://physionet.org/), which are open-access and do not involve any ethical or private problems of patients.

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The authors declare no competing interests.

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Cite this article

Jin, Z., Jia, K. A temporal multi-scale hybrid attention network for sleep stage classification. Med Biol Eng Comput 61, 2291–2303 (2023). https://doi.org/10.1007/s11517-023-02808-z

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