Real-time Assessment of Obstructive Sleep Apnea Using Deep Learning

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Sleep quality assessments provide various measures to gauge the severity of Sleep Apnea. In the present, sleep quality testing is inconvenient for the patients in terms of both money and a comfortable environment. Evaluation methods like the Polysomnography test require many sensing resources. Our research proposes an inexpensive and an automated system based on Single-lead Electrocardiogram (ECG) signal and a one-dimensional Convolutional Neural Network classifier (CNN). We use only a single-channel ECG to measure the heart signal and deliver them to an 1D-CNN to classify for apneic events. This method provides an alternative to the cumbersome and expensive Polysomnography (PSG) and scoring by Rechtschaffen and Kales visual method. In addition to this, we propose an Android application that uses a Deep Neural Network model that we have trained to use in real assessment of Obstructive Sleep Apnea.

Sleep apnea syndromes, Electrocardiography, Deep learning (Machine learning), Mobile apps