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Convolution- and Attention-Based Neural Network for Automated Sleep Stage Classification

Author

Listed:
  • Tianqi Zhu

    (College of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou 310027, China)

  • Wei Luo

    (College of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou 310027, China)

  • Feng Yu

    (College of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou 310027, China)

Abstract

Analyzing polysomnography (PSG) is an effective method for evaluating sleep health; however, the sleep stage scoring required for PSG analysis is a time-consuming effort for an experienced medical expert. When scoring sleep epochs, experts pay attention to find specific signal characteristics (e.g., K-complexes and spindles), and sometimes need to integrate information from preceding and subsequent epochs in order to make a decision. To imitate this process and to build a more interpretable deep learning model, we propose a neural network based on a convolutional network (CNN) and attention mechanism to perform automatic sleep staging. The CNN learns local signal characteristics, and the attention mechanism excels in learning inter- and intra-epoch features. In experiments on the public sleep-edf and sleep-edfx databases with different training and testing set partitioning methods, our model achieved overall accuracies of 93.7% and 82.8%, and macro-average F1-scores of 84.5 and 77.8, respectively, outperforming recently reported machine learning-based methods.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jijerp:v:17:y:2020:i:11:p:4152-:d:369793
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    References listed on IDEAS

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    1. 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. 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.
    2. Chengfan Li & Yueyu Qi & Xuehai Ding & Junjuan Zhao & Tian Sang & Matthew Lee, 2022. "A Deep Learning Method Approach for Sleep Stage Classification with EEG Spectrogram," IJERPH, MDPI, vol. 19(10), pages 1-17, May.

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