Evaluation of a deep learning sleep staging algorithm utilizing a single frontal EEG channel on a clinical population with suspected or known Obstructive Sleep Apnea.

Abstract

Home Sleep Apnea Testing (HSAT) for the diagnosis of Obstructive Sleep Apnea (OSA) has emerged as a simpler and more cost-effective diagnostic option compared with attended in-lab Polysomnography (PSG). The identification of sleep stages form an essential part of the OSA diagnosis as it allows for proper phenotyping of OSA, specifically the REM phenotype. The manual staging of sleep is arduous and costly, so the development of accurate Deep Learning (DL) algorithms the automatically classify sleep stages forms a crucial role in the diagnosis of OSA with HSAT. The purpose of this study is to investigate the accuracy of a DL sleep staging algorithm in a new miniaturized sleep monitoring device – Somfit® Compumedics Ltd. This device is attached to the patient’s forehead using an adhesive electrode patch (See Figure 2). This patch records one EEG channel (Fp1 – Fp2), two EOG channels (EOG-R, EOG-L), one EMG channel (frontalis) with additional channels recorded onboard the Somfit ® module (oximetry, PAT, pulse rate, snore sounds, head positioning, actigraphy).