Obstructive sleep apnea detection using SVM-based classification of ECG signal features

• Published in: 2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society Vol. 2012; pp. 4938 - 4941
• Main Authors: Almazaydeh, L., Elleithy, K., Faezipour, M.
• Format: Conference Proceeding Journal Article
• Language: English
• Published: United States IEEE 01.01.2012
• Subjects: Accuracy; Algorithms; Diagnosis, Computer-Assisted - methods; ECG; Electrocardiography; Electrocardiography - methods; Feature extraction; Heart Rate; Humans; Pattern Recognition, Automated - methods; Polysomnography - methods; PSG; Reproducibility of Results; RR interval; Sensitivity and Specificity; Sleep apnea; Sleep Apnea, Obstructive - diagnosis; Sleep Apnea, Obstructive - physiopathology; Support Vector Machine; Support vector machines; SVM
• ISBN: 1424441196; 9781424441198
• ISSN: 1094-687X; 1557-170X
• DOI: 10.1109/EMBC.2012.6347100

Sleep apnea is the instance when one either has pauses of breathing in their sleep, or has very low breath while asleep. This pause in breathing can range in frequency and duration. Obstructive sleep apnea (OSA) is the common form of sleep apnea, which is currently tested through polysomnography (PSG) at sleep labs. PSG is both expensive and inconvenient as an expert human observer is required to work over night. New sleep apnea classification techniques are nowadays being developed by bioengineers for most comfortable and timely detection. This paper focuses on an automated classification algorithm which processes short duration epochs of the electrocardiogram (ECG) data. The presented classification technique is based on support vector machines (SVM) and has been trained and tested on sleep apnea recordings from subjects with and without OSA. The results show that our automated classification system can recognize epochs of sleep disorders with a high accuracy of 96.5% or higher. Furthermore, the proposed system can be used as a basis for future development of a tool for OSA screening.