An Accurate and Low-power Sleep Apnea Detector in the Edge-IoT Platform

Sleep apnea (SA) is a serious sleep disorder that causes various diseases such as hypertension, type 2 diabetes, depression, and obesity. It may lead to serious health issues, including stroke if left untreated. The monitoring of SA through existing medical devices takes a lot of time and lacks the user's convenience. In this paper, an Internet of Things (IoT) enabled and automated SA detection technique has been presented that uses a deep-learning algorithm to analyze single-channel electrocardiogram (ECG) data and classify SA events from the specified epoch. SA detection is performed in two stages. In the first stage, a 1-sec epoch is extracted from the ECG signal, which is supplied for feature extraction. In the second stage, SA pattern is trained offline using a deep neural network (DNN), and real-time SA detection is performed during the testing phase. The widely available open-source dataset from PhysioNet is utilized for validation purposes. The proposed approach enhances the current state of the art and makes it a potential candidate for wearable medical devices.