SVM-Based Sleep Apnea Identification Using Optimal RR-Interval Features of the ECG Signal
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Authors
Almazaydeh, Laiali
Elleithy, Khaled M.
Faezipour, Miad
Ocbagabir, Helen
Issue Date
2016-03
Type
Article
Language
en_US
Keywords
Sleep apnea , Polysomnography (PSG) , Electrocardiography (ECG) , RR interval , Features extraction , Support vector machine
Alternative Title
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.
Description
Citation
Publisher
Advanced Technoloy and Science