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A highly Reliable and Fully Automated Classification System for Sleep Apnea Detection

Year 2016, Volume: 4 Issue: 3, 66 - 70, 01.11.2016
https://doi.org/10.18201/ijisae.47487

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.

Year 2016, Volume: 4 Issue: 3, 66 - 70, 01.11.2016
https://doi.org/10.18201/ijisae.47487

Abstract

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Details

Journal Section Research Article
Authors

Laiali Almazaydeh

Khaled Elleithy This is me

Miad Faezipour This is me

Publication Date November 1, 2016
Published in Issue Year 2016 Volume: 4 Issue: 3

Cite

APA Almazaydeh, L., Elleithy, K., & Faezipour, M. (2016). A highly Reliable and Fully Automated Classification System for Sleep Apnea Detection. International Journal of Intelligent Systems and Applications in Engineering, 4(3), 66-70. https://doi.org/10.18201/ijisae.47487
AMA Almazaydeh L, Elleithy K, Faezipour M. A highly Reliable and Fully Automated Classification System for Sleep Apnea Detection. International Journal of Intelligent Systems and Applications in Engineering. November 2016;4(3):66-70. doi:10.18201/ijisae.47487
Chicago Almazaydeh, Laiali, Khaled Elleithy, and Miad Faezipour. “A Highly Reliable and Fully Automated Classification System for Sleep Apnea Detection”. International Journal of Intelligent Systems and Applications in Engineering 4, no. 3 (November 2016): 66-70. https://doi.org/10.18201/ijisae.47487.
EndNote Almazaydeh L, Elleithy K, Faezipour M (November 1, 2016) A highly Reliable and Fully Automated Classification System for Sleep Apnea Detection. International Journal of Intelligent Systems and Applications in Engineering 4 3 66–70.
IEEE L. Almazaydeh, K. Elleithy, and M. Faezipour, “A highly Reliable and Fully Automated Classification System for Sleep Apnea Detection”, International Journal of Intelligent Systems and Applications in Engineering, vol. 4, no. 3, pp. 66–70, 2016, doi: 10.18201/ijisae.47487.
ISNAD Almazaydeh, Laiali et al. “A Highly Reliable and Fully Automated Classification System for Sleep Apnea Detection”. International Journal of Intelligent Systems and Applications in Engineering 4/3 (November 2016), 66-70. https://doi.org/10.18201/ijisae.47487.
JAMA Almazaydeh L, Elleithy K, Faezipour M. A highly Reliable and Fully Automated Classification System for Sleep Apnea Detection. International Journal of Intelligent Systems and Applications in Engineering. 2016;4:66–70.
MLA Almazaydeh, Laiali et al. “A Highly Reliable and Fully Automated Classification System for Sleep Apnea Detection”. International Journal of Intelligent Systems and Applications in Engineering, vol. 4, no. 3, 2016, pp. 66-70, doi:10.18201/ijisae.47487.
Vancouver Almazaydeh L, Elleithy K, Faezipour M. A highly Reliable and Fully Automated Classification System for Sleep Apnea Detection. International Journal of Intelligent Systems and Applications in Engineering. 2016;4(3):66-70.