A deep learning model for automated sleep stages classification using PSG signals

Ozal Yildirim*, Ulas Baran Baloglu, U. Rajendra Acharya

*Corresponding author for this work

Research output: Contribution to journalArticle (Academic Journal)peer-review

170 Citations (Scopus)
286 Downloads (Pure)


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.

Original languageEnglish
Article number599
Number of pages21
JournalInternational Journal of Environmental Research and Public Health
Issue number4
Publication statusPublished - 19 Feb 2019


  • Classification
  • CNNs
  • Deep learning
  • Polysomnography (PSG)
  • Sleep stages
  • Electrooculography
  • Automation
  • Humans
  • Polysomnography/methods
  • Electroencephalography
  • Deep Learning
  • Sleep Stages
  • Sleep Wake Disorders/physiopathology
  • Neural Networks (Computer)


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