Automated recognition of obstructive sleep apnea using ensemble support vector machine classifier
by V. Kalaivani
International Journal of Biomedical Engineering and Technology (IJBET), Vol. 33, No. 3, 2020

Abstract: ECG is mainly used to diagnosis the obstructive sleep apnea (OSA) with a high degree of accuracy in clinical care applications. We have developed a real-time algorithm for the detection of sleep apnea disease based on electrocardiograph (ECG). In this study, features from ECG signals were extracted from 12 normal and 58 OSA patients from physionet apnea ECG database. The baseline noise, motion drift and muscle noise in raw ECG signals are removed using median filter and Daubechies wavelet filter. QRS detection algorithm extracts R-wave amplitude and R-wave time duration from de-noised signal. The proposed QRS detection algorithm contains four stages. The stages are calculation of QRS-complex slope, squaring function, moving-window integration and calculation of R-peak and QRS detection. Time domain features are calculated from the heart rate variability and ECG-derived respiration (EDR). Support vector machine (SVM) and ensemble support vector machine techniques are used for the detection of OSA.

Online publication date: Wed, 17-Jun-2020

The full text of this article is only available to individual subscribers or to users at subscribing institutions.

 
Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.

Pay per view:
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.

Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Biomedical Engineering and Technology (IJBET):
Login with your Inderscience username and password:

    Username:        Password:         

Forgotten your password?


Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.

If you still need assistance, please email subs@inderscience.com