Received: 24 July 2015 Revised: 11 February 2017 Accepted: 11 July 2017 DOI: 10.1111/coin.12138 ORIGINAL ARTICLE Detection and classification of sleep apnea using genetic algorithms and SVM-based classification of thoracic respiratory effort and oximetric signal features Zahra Abedi 1 Nadia Naghavi 1 Fariborz Rezaeitalab 2 1 Department of Electrical Engineering, Ferdowsi University of Mashhad, Mashhad, Iran 2 Department of Neurology, School of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran Correspondence Nadia Naghavi, Department of Electrical Engineering, Ferdowsi University of Mashhad, Azadi Square, PO Box 91779 48974, Mashhad, Khorasan Razavi, Iran. Email: n.naghavi@um.ac.ir Abstract Sleep apnea is a relatively prevalent breathing disorder char- acterized by temporary interruptions in airflow during sleep. There are 2 major types of sleep apnea. Obstructive sleep apnea occurs when air cannot flow through the upper airway despite efforts to breathe. Central sleep apnea occurs when the brain fails to signal to the muscles to maintain breath- ing. The standard diagnostic test is polysomnography, which is expensive and time consuming. The aim of this study was to design an automatic diagnostic and classifying algorithm for sleep apneas employing thoracic respiratory effort and oxi- metric signals. This algorithm was trained and tested applying a database of 54 subjects who had undergone polysomnog- raphy. A feature extraction stage was conducted to compute features. An optimal genetic algorithm was applied to select optimal features of these 2 kinds of signals. The classification technique was based on the support vector machine classi- fier to classify the selected features in 3 classes as healthy, obstructive, and central sleep apnea events. The results show that our automated classification algorithm can diagnose sleep apnea and its types with an average accuracy level of 90.2% (87.5-95.8) in the test set and 90.9% in the validation set with high acceptable accuracy. KEYWORDS genetic algorithm, oximetric signal, sleep apnea, support vector machine, thoracic respiratory effort signal 1 INTRODUCTION Until the mid-twentieth century, it was generally believed that sleep is an inactive period in the membrane physiology. However, nowadays, through the development of technology, it is believed Computational Intelligence. 2017;1–14. wileyonlinelibrary.com/journal/coin © 2017 Wiley Periodicals, Inc. 1