A highly Reliable and Fully Automated Classification System for Sleep Apnea Detection
Journal Title: International Journal of Intelligent Systems and Applications in Engineering - Year 2016, Vol 4, Issue 3
Abstract
Sleep apnea (SA) in the form of Obstructive sleep apnea (OSA) is becoming the most common respiratory disorder during sleep, which is characterized by cessations of airflow to the lungs. These cessations in breathing must last more than 10 seconds to be considered an apnea event. Apnea events may occur 5 to 30 times an hour and may occur up to four hundred times per night in those with severe SA [1]. Nowadays, polysomnography (PSG) is a standard testing procedure to diagnose OSA which includes the monitoring of the breath airflow, respiratory movement, and oxygen saturation (SpO2), body position, electroencephalography (EEG), electromyography (EMG), electrooculography (EOG), and electrocardiography (ECG). Therefore, a final diagnosis decision is obtained by means of medical examination of these recordings [2]. However, new simplified diagnostic methods and continuous screening of OSA is needed in order to have a major benefit of the treatment on OSA outcomes. In this regard, a portable monitoring system is developed to facilitate the self-administered sleep tests in familiar surroundings environment closer to the patients’ normal sleep habits. With only three data channels: tracheal breathing sounds, ECG and SpO2 signals, a patient does not need hospitalization and can be diagnosed and receive feedback at home, which eases follow-up and retesting after treatment.
Authors and Affiliations
Laiali Almazaydeh| Department of Software Engineering, Al-Hussein Bin Talal University, Ma’an, Jordan, Khaled Elleithy| Department of Computer Science and Engineering, University of Bridgeport, Bridgeport, CT 06604, USA, Miad Faezipour| Department of Computer Science and Engineering, University of Bridgeport, Bridgeport, CT 06604, USA
Keywords
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- EP ID EP801
- DOI 10.18201/ijisae.47487
- Views 388
- Downloads 22
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