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Automated Sleep Stages Classification Using Convolutional Neural Network From Raw and Time-Frequency Electroencephalogram Signals: Systematic Evaluation Study

Automated Sleep Stages Classification Using Convolutional Neural Network From Raw and Time-Frequency Electroencephalogram Signals: Systematic Evaluation Study

Polysomnography (PSG) is the gold standard for assessing sleep quality and diagnosing sleep disorders. PSG sleep staging requires visual inspection of electroencephalogram (EEG), electromyogram, and electrooculogram data, which is time-consuming and labor-intensive, that is, trained technicians may spend hours manually scoring a single night of sleep [1,2]. Thus, the resultant high cost of PSG makes it an unappealing method for longitudinal or population-based sleep studies.

Shahab Haghayegh, Kun Hu, Katie Stone, Susan Redline, Eva Schernhammer

J Med Internet Res 2023;25:e40211