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

Ozal Yildirim, Ulas Baran Baloglu and U Rajendra Acharya
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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

IJERPH, 2019, vol. 16, issue 4, 1-21

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.

Keywords: sleep stages; classification; deep learning; CNNs; polysomnography (PSG) (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2019
References: View complete reference list from CitEc
Citations: View citations in EconPapers (5)

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