BibTex RIS Kaynak Göster

SVM-Based Sleep Apnea Identification Using Optimal RR-Interval Features of the ECG Signal

Yıl 2016, , 1 - 4, 31.03.2016
https://doi.org/10.18201/ijisae.79075

Öz

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.  

Yıl 2016, , 1 - 4, 31.03.2016
https://doi.org/10.18201/ijisae.79075

Öz

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Ayrıntılar

Bölüm Research Article
Yazarlar

Laiali Almazaydeh

Khaled Elleithy Bu kişi benim

Miad Faezipour Bu kişi benim

Helen Ocbagabir Bu kişi benim

Yayımlanma Tarihi 31 Mart 2016
Yayımlandığı Sayı Yıl 2016

Kaynak Göster

APA Almazaydeh, L., Elleithy, K., Faezipour, M., Ocbagabir, H. (2016). SVM-Based Sleep Apnea Identification Using Optimal RR-Interval Features of the ECG Signal. International Journal of Intelligent Systems and Applications in Engineering, 4(1), 1-4. https://doi.org/10.18201/ijisae.79075
AMA Almazaydeh L, Elleithy K, Faezipour M, Ocbagabir H. SVM-Based Sleep Apnea Identification Using Optimal RR-Interval Features of the ECG Signal. International Journal of Intelligent Systems and Applications in Engineering. Mart 2016;4(1):1-4. doi:10.18201/ijisae.79075
Chicago Almazaydeh, Laiali, Khaled Elleithy, Miad Faezipour, ve Helen Ocbagabir. “SVM-Based Sleep Apnea Identification Using Optimal RR-Interval Features of the ECG Signal”. International Journal of Intelligent Systems and Applications in Engineering 4, sy. 1 (Mart 2016): 1-4. https://doi.org/10.18201/ijisae.79075.
EndNote Almazaydeh L, Elleithy K, Faezipour M, Ocbagabir H (01 Mart 2016) SVM-Based Sleep Apnea Identification Using Optimal RR-Interval Features of the ECG Signal. International Journal of Intelligent Systems and Applications in Engineering 4 1 1–4.
IEEE L. Almazaydeh, K. Elleithy, M. Faezipour, ve H. Ocbagabir, “SVM-Based Sleep Apnea Identification Using Optimal RR-Interval Features of the ECG Signal”, International Journal of Intelligent Systems and Applications in Engineering, c. 4, sy. 1, ss. 1–4, 2016, doi: 10.18201/ijisae.79075.
ISNAD Almazaydeh, Laiali vd. “SVM-Based Sleep Apnea Identification Using Optimal RR-Interval Features of the ECG Signal”. International Journal of Intelligent Systems and Applications in Engineering 4/1 (Mart 2016), 1-4. https://doi.org/10.18201/ijisae.79075.
JAMA Almazaydeh L, Elleithy K, Faezipour M, Ocbagabir H. SVM-Based Sleep Apnea Identification Using Optimal RR-Interval Features of the ECG Signal. International Journal of Intelligent Systems and Applications in Engineering. 2016;4:1–4.
MLA Almazaydeh, Laiali vd. “SVM-Based Sleep Apnea Identification Using Optimal RR-Interval Features of the ECG Signal”. International Journal of Intelligent Systems and Applications in Engineering, c. 4, sy. 1, 2016, ss. 1-4, doi:10.18201/ijisae.79075.
Vancouver Almazaydeh L, Elleithy K, Faezipour M, Ocbagabir H. SVM-Based Sleep Apnea Identification Using Optimal RR-Interval Features of the ECG Signal. International Journal of Intelligent Systems and Applications in Engineering. 2016;4(1):1-4.