BibTex RIS Kaynak Göster

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

Yıl 2016, , 66 - 70, 01.11.2016
https://doi.org/10.18201/ijisae.47487

Öz

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.

Yıl 2016, , 66 - 70, 01.11.2016
https://doi.org/10.18201/ijisae.47487

Öz

Toplam 0 adet kaynakça vardır.

Ayrıntılar

Bölüm Research Article
Yazarlar

Laiali Almazaydeh

Khaled Elleithy Bu kişi benim

Miad Faezipour Bu kişi benim

Yayımlanma Tarihi 1 Kasım 2016
Yayımlandığı Sayı Yıl 2016

Kaynak Göster

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. Kasım 2016;4(3):66-70. doi:10.18201/ijisae.47487
Chicago Almazaydeh, Laiali, Khaled Elleithy, ve Miad Faezipour. “A Highly Reliable and Fully Automated Classification System for Sleep Apnea Detection”. International Journal of Intelligent Systems and Applications in Engineering 4, sy. 3 (Kasım 2016): 66-70. https://doi.org/10.18201/ijisae.47487.
EndNote Almazaydeh L, Elleithy K, Faezipour M (01 Kasım 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, ve M. Faezipour, “A highly Reliable and Fully Automated Classification System for Sleep Apnea Detection”, International Journal of Intelligent Systems and Applications in Engineering, c. 4, sy. 3, ss. 66–70, 2016, doi: 10.18201/ijisae.47487.
ISNAD Almazaydeh, Laiali vd. “A Highly Reliable and Fully Automated Classification System for Sleep Apnea Detection”. International Journal of Intelligent Systems and Applications in Engineering 4/3 (Kasım 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 vd. “A Highly Reliable and Fully Automated Classification System for Sleep Apnea Detection”. International Journal of Intelligent Systems and Applications in Engineering, c. 4, sy. 3, 2016, ss. 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.