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A Deep Learning Model for Automated Sleep Stages Classification Using PSG Signals

Author

Listed:
  • Ozal Yildirim

    (Department of Computer Engineering, Munzur University, Tunceli 62000, Turkey)

  • Ulas Baran Baloglu

    (Department of Computer Engineering, Munzur University, Tunceli 62000, Turkey)

  • U Rajendra Acharya

    (Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Singapore 599489, Singapore
    Department of Biomedical Engineering, School of Science and Technology, Singapore School of Social Sciences, Singapore 599489, Singapore
    School of Medicine, Faculty of Health and Medical Sciences, Taylor’s University, Subang Jaya 47500, Malaysia)

Abstract

Sleep disorder is a symptom of many neurological diseases that may significantly affect the quality of daily life. Traditional methods are time-consuming and involve the manual scoring of polysomnogram (PSG) signals obtained in a laboratory environment. However, the automated monitoring of sleep stages can help detect neurological disorders accurately as well. In this study, a flexible deep learning model is proposed using raw PSG signals. A one-dimensional convolutional neural network (1D-CNN) is developed using electroencephalogram (EEG) and electrooculogram (EOG) signals for the classification of sleep stages. The performance of the system is evaluated using two public databases (sleep-edf and sleep-edfx). The developed model yielded the highest accuracies of 98.06%, 94.64%, 92.36%, 91.22%, and 91.00% for two to six sleep classes, respectively, using the sleep-edf database. Further, the proposed model obtained the highest accuracies of 97.62%, 94.34%, 92.33%, 90.98%, and 89.54%, respectively for the same two to six sleep classes using the sleep-edfx dataset. The developed deep learning model is ready for clinical usage, and can be tested with big PSG data.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jijerp:v:16:y:2019:i:4:p:599-:d:207111
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    Citations

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    Cited by:

    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. Manish Sharma & Anuj Yadav & Jainendra Tiwari & Murat Karabatak & Ozal Yildirim & U. Rajendra Acharya, 2022. "An Automated Wavelet-Based Sleep Scoring Model Using EEG, EMG, and EOG Signals with More Than 8000 Subjects," IJERPH, MDPI, vol. 19(12), pages 1-12, June.
    3. Afshin Shoeibi & Marjane Khodatars & Navid Ghassemi & Mahboobeh Jafari & Parisa Moridian & Roohallah Alizadehsani & Maryam Panahiazar & Fahime Khozeimeh & Assef Zare & Hossein Hosseini-Nejad & Abbas K, 2021. "Epileptic Seizures Detection Using Deep Learning Techniques: A Review," IJERPH, MDPI, vol. 18(11), pages 1-33, May.
    4. Manish Sharma & Jainendra Tiwari & U. Rajendra Acharya, 2021. "Automatic Sleep-Stage Scoring in Healthy and Sleep Disorder Patients Using Optimal Wavelet Filter Bank Technique with EEG Signals," IJERPH, MDPI, vol. 18(6), pages 1-29, March.
    5. 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.

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