NDLI: Sleep apnea classification using least-squares support vector machines on single lead ECG

Content Provider	IEEE Xplore Digital Library
Author	Varon, C. Testelmans, D. Buyse, B. Suykens, J.A.K. Van Huffel, S.
Copyright Year	2013
Description	Author affiliation: Dept. of Electr. Eng., KU Leuven, Leuven, Belgium (Varon, C.; Suykens, J.A.K.; Van Huffel, S.) \|\| Dept. of Pneumology, UZ Leuven, Leuven, Belgium (Testelmans, D.; Buyse, B.)
Abstract	In this paper a methodology to identify sleep apnea events is presented. It uses four easily computable features, three generally known ones and a newly proposed feature. Of the three well known parameters, two are computed from the RR interval time series and the other one from the approximate respiratory signal derived from the ECG using principal component analysis (PCA). The fourth feature is proposed in this paper and it is computed from the principal components of the QRS complexes. Together with a least squares support vector machines (LS-SVM) classifier using an RBF kernel, these four features achieve an accuracy on test data larger than 85% for a subject independent classification, and of more than 90% for a patient specific approach. These values are comparable with other results in the literature, but have the advantage that their computation is straightforward and much simpler. This can be important when implemented in a home monitoring system, which typically has limited computational resources.
Starting Page	5029
Ending Page	5032
File Size	201656
Page Count	4
File Format	PDF
ISBN	9781457702167
ISSN	1557170X
DOI	10.1109/EMBC.2013.6610678
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-07-03
Publisher Place	Japan
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Electrocardiography Sleep apnea Support vector machines Principal component analysis Kernel Feature extraction Covariance matrices
Content Type	Text
Resource Type	Article
Subject	Signal Processing Biomedical Engineering Health Informatics Computer Vision and Pattern Recognition

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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