SVM-Based Sleep Apnea Identification Using Optimal RR-Interval Features of the ECG Signal
Journal Title: International Journal of Intelligent Systems and Applications in Engineering - Year 2016, Vol 4, Issue 1
Abstract
Sleep apnea (SA) is the most commonly known sleeping disorder characterized by pauses of airflow to the lungs and often results in day and night time symptoms such as impaired concentration, depression, memory loss, snoring, nocturnal arousals, sweating and restless sleep. Obstructive Sleep Apnea (OSA), the most common SA, is a result of a collapsed upper respiratory airway, which is majorly undiagnosed due to the inconvenient Polysomnography (PSG) testing procedure at sleep labs. This paper introduces an automated approach towards identifying sleep apnea. The idea is based on efficient feature extraction of the electrocardiogram (ECG) signal by employing a hybrid of signal processing techniques and classification using a linear-kernel Support Vector Machine (SVM). The optimum set of RR-interval features of the ECG signal yields a high classification accuracy of 97.1% when tested on the Physionet Apnea-ECG recordings. The results provide motivating insights towards future developments of convenient and effective OSA screening setups.
Authors and Affiliations
Laiali Almazaydeh| Department of Software Engineering, Al Hussein Bin Talal University, Jordan, Khaled Elleithy| Department of Computer Science and Engineering, University of Bridgeport, CT 06604, USA, Miad Faezipour| Department of Computer Science and Engineering, University of Bridgeport, CT 06604, USA, Helen Ocbagabir| Department of Computer Science and Engineering, University of Bridgeport, CT 06604, USA
Keywords
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- EP ID EP789
- DOI 10.18201/ijisae.79075
- Views 450
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