Classification of Obstructive Sleep Apnea using Multimodal and Sigma-based Feature Representation

Abstract

Obstructive sleep apnea (OSA) is a sleep disorder characterized by a decrease in blood oxygen saturation and waking up after a long time. Diagnosis can be made by following a full night with a polysomnogram device, so there is a need for computer-based methods for the diagnosis of OSA. In this study, a method based on feature selection is proposed for OSA classification using oxygen saturation and electrocardiogram signals. Standard deviation (sigma) based features have been created to increase accuracy and reduce computational complexity. To evaluate the effectiveness, comparisons were made with selected machine learning algorithms. The achievements of the obtained features were compared with Naive Bayes (NB), k-nearest neighborhood (kNN) and Support Vector Machine (SVM) classifiers. The tests performed on the PhysioNet dataset consisting of real clinical samples show that the use of sigma-based features result an average performance increase of 1.98% in all test scenarios.