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CAttSleepNet: Automatic End-to-End Sleep Staging Using Attention-Based Deep Neural Networks on Single-Channel EEG

Author

Listed:
  • Tingting Li

    (School of Computer Engineering and Science, Shanghai University, Shanghai 200444, China)

  • Bofeng Zhang

    (School of Computer and Communication Engineering, Shanghai Polytechnic University, Shanghai 201209, China
    School of Computer Science and Technology, Kashi University, Kashi 844008, China)

  • Hehe Lv

    (School of Computer Engineering and Science, Shanghai University, Shanghai 200444, China)

  • Shengxiang Hu

    (School of Computer Engineering and Science, Shanghai University, Shanghai 200444, China)

  • Zhikang Xu

    (School of Computer Engineering and Science, Shanghai University, Shanghai 200444, China)

  • Yierxiati Tuergong

    (School of Computer Science and Technology, Kashi University, Kashi 844008, China)

Abstract

Accurate sleep staging results can be used to measure sleep quality, providing a reliable basis for the prevention and diagnosis of sleep-related diseases. The key to sleep staging is the feature representation of EEG signals. Existing approaches rarely consider local features in feature extraction, and fail to distinguish the importance of critical and non-critical local features. We propose an innovative model for automatic sleep staging with single-channel EEG, named CAttSleepNet. We add an attention module to the convolutional neural network (CNN) that can learn the weights of local sequences of EEG signals by exploiting intra-epoch contextual information. Then, a two-layer bidirectional-Long Short-Term Memory (Bi-LSTM) is used to encode the global correlations of successive epochs. Therefore, the feature representations of EEG signals are enhanced by both local and global context correlation. Experimental results achieved on two real-world sleep datasets indicate that the CAttSleepNet model outperforms existing models. Moreover, ablation experiments demonstrate the validity of our proposed attention module.

Suggested Citation

  • Tingting Li & Bofeng Zhang & Hehe Lv & Shengxiang Hu & Zhikang Xu & Yierxiati Tuergong, 2022. "CAttSleepNet: Automatic End-to-End Sleep Staging Using Attention-Based Deep Neural Networks on Single-Channel EEG," IJERPH, MDPI, vol. 19(9), pages 1-15, April.
    • Handle: RePEc:gam:jijerp:v:19:y:2022:i:9:p:5199-:d:801548
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    References listed on IDEAS

    as
    1. Tianqi Zhu & Wei Luo & Feng Yu, 2020. "Convolution- and Attention-Based Neural Network for Automated Sleep Stage Classification," IJERPH, MDPI, vol. 17(11), pages 1-13, June.
    2. Ozal Yildirim & Ulas Baran Baloglu & U Rajendra Acharya, 2019. "A Deep Learning Model for Automated Sleep Stages Classification Using PSG Signals," IJERPH, MDPI, vol. 16(4), pages 1-21, February.
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    Cited by:

    1. Bocheng Li & Yunqiu Zhang & Xusheng Wu, 2022. "DLKN-MLC: A Disease Prediction Model via Multi-Label Learning," IJERPH, MDPI, vol. 19(15), pages 1-15, August.

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