Ehsan Sadeghipour - Sama technical and vocational training college, Islamic Azad University, Bandar Abbas Branch, Bandar Abbas, Iran, Ahmad Hatam - University of Hormozgan, Faculty of Power and Computer Engineering, University Hormozgan, Bandar Abbas, Iran, Farzad Hosseinzadeh - Sama technical and vocational training college, Islamic Azad University, Bandar Abbas Branch, Bandar Abbas, Iran,
Obstructive sleep apnea is a common condition with serious neural-psychological complications and cardiovascular problems if not diagnosed and treated in time. Despite the importance of this disease in our country, it has not received much attention and there are few centers for evaluating patients suffering from it. In this article, an intelligent method is introduced for diagnosing obstructive sleep apnea that uses features extracted from changes in heart rate and respiratory signals in the ECG as input for training and testing the modified XCS classifier system. Comparison of results obtained from implementing the mentioned method with those of other methods on physionet database showed desirable performance and high accuracy of the proposed system in diagnosing obstructive sleep apnea.
Ehsan Sadeghipour, Ahmad Hatam, Farzad Hosseinzadeh, An Expert Clinical System for Diagnosing Obstructive Sleep Apnea with Help From the Xcsr Classifier, Journal of Mathematics and Computer Science, 14 (2015), no. 1, 33-41
Sadeghipour Ehsan, Hatam Ahmad, Hosseinzadeh Farzad, An Expert Clinical System for Diagnosing Obstructive Sleep Apnea with Help From the Xcsr Classifier. J Math Comput SCI-JM. (2015); 14(1):33-41
Sadeghipour, Ehsan, Hatam, Ahmad, Hosseinzadeh, Farzad. "An Expert Clinical System for Diagnosing Obstructive Sleep Apnea with Help From the Xcsr Classifier." Journal of Mathematics and Computer Science, 14, no. 1 (2015): 33-41
