A Deep Learning Model for Automated Sleep Stages Classification Using PSG Signals
| Autor: | Ozal Yildirim, U. Rajendra Acharya, Ulas Baran Baloglu |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2019 |
| Předmět: |
Sleep Wake Disorders
Computer science Health Toxicology and Mutagenesis Polysomnography Polysomnogram lcsh:Medicine Polysomnography/methods 02 engineering and technology sleep stages Electroencephalography Convolutional neural network Article 03 medical and health sciences Automation 0302 clinical medicine Polysomnography (PSG) 0202 electrical engineering electronic engineering information engineering medicine Humans Sleep Wake Disorders/physiopathology Sleep Stages Sleep disorder medicine.diagnostic_test business.industry Deep learning lcsh:R Public Health Environmental and Occupational Health deep learning CNNs Pattern recognition Electrooculography polysomnography (PSG) Neural Networks (Computer) Classification medicine.disease classification 020201 artificial intelligence & image processing Sleep (system call) Artificial intelligence Neural Networks Computer business 030217 neurology & neurosurgery |
| Zdroj: | International Journal of Environmental Research and Public Health Volume 16 Issue 4 International Journal of Environmental Research and Public Health, Vol 16, Iss 4, p 599 (2019) Yildirim, O, Baloglu, U B & Acharya, U R 2019, ' A deep learning model for automated sleep stages classification using PSG signals ', International Journal of Environmental Research and Public Health, vol. 16, no. 4, 599 . https://doi.org/10.3390/ijerph16040599 |
| ISSN: | 1660-4601 |
| DOI: | 10.3390/ijerph16040599 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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