ECG classification for sleep apnea detection

Sleep apnea is a sleep-related breathing disorder that involves a decrease or complete halt in airflow despite an ongoing effort to breathe. The most common form of sleep apnea is well known as Obstructive sleep apnea (OSA) which is currently diagnosed using polysomnography (PSG) at sleeping labs. This diagnostic technique is both expensive and inconvenient. It requires an expert human to observe the patient over night. New automated methods have been developed for sleep apnea detection using artificial intelligence algorithms, which are more convenient and comfortable for patients. The aim of this paper is two folds: first, compare the well-known methods that have been proposed in the literature, which may have not used the same features and/or the same dataset. Secondly, Use a variety of classifiers which may have not been previously explored. In this paper, we will explore different type of classifiers for sleep apnea detection. Statistical features have been extracted and fed into different types of classifiers. The results show that the KNN classifier reaches an accuracy of 98.7% overpassing all the other classifiers that have been already used in the literature.