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

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ID: 85534
2019
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Ranked #339 of 862 articles by views in International journal of environmental research and public health

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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.
Reference Key
yildirim2019ainternational Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Yildirim, Ozal;Baloglu, Ulas Baran;Acharya, U Rajendra;
Journal International journal of environmental research and public health
Year 2019
DOI
E599
URL
Keywords
Deep learning classification cnns polysomnography (psg) sleep stages gnss-based passive radar long integration time maritime surveillance passive radar track-before-detect

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