NDLI: Multi-class SVM classification combined with kernel PCA feature extraction of ECG signals

Content Provider	IEEE Xplore Digital Library
Author	Kallas, M. Francis, C. Kanaan, L. Merheb, D. Honeine, P. Amoud, H.
Copyright Year	2012
Description	Author affiliation: Laboratoire d'Analyse et de Surveillance des Systèmes (LASYS), Faculty of Engineering, Lebanese University, Lebanon (Kallas, M.; Francis, C.) \|\| Department of Telecommunications, Faculty of Engineering, Holy Spirit University of Kaslik, Lebanon (Kanaan, L.; Merheb, D.) \|\| Institut Charles Delaunay (UMR CNRS 6279), Université de Technologie de Troyes, France (Honeine, P.) \|\| Azm Center for Research in Biotechnology and its Applications, Lebanese University, Lebanon (Amoud, H.)
Abstract	The cardiovascular diseases are one of the main causes of death around the world. Automatic detection and classification of electrocardiogram (ECG) signals are important for diagnosis of cardiac irregularities. This paper proposes to apply the Support Vector Machines (SVM) classification, to diagnose heartbeat abnormalities, after performing feature extraction on the ECG signals. The experiments were conducted on the ECG signals from the MIT-BIH arrhythmia database [1] to classify two different abnormalities and normal beats. Kernel Principal Component Analysis (KPCA) is used for feature extraction since it performes better than PCA on ECG signals due to their nonlinear structures. This is demonstrated in a previous work [2]. Two multi-SVM classification schemes are used, One-Against-One (OAO) and One-Against-All (OAA), to classify the ECG signals into different disease categories. The experiments conducted show that SVM combined with KPCA performs better than that without feature extraction. Moreover, our results show a better performance in Gaussian KPCA feature extraction with respect to other kernels. Furthermore,the performance of Gaussian OAA-SVM combined with KPCA has higher average accuracy than Gaussian OAA-SVM in ECG classification.
Starting Page	1
Ending Page	5
File Size	277813
Page Count	5
File Format	PDF
ISBN	9781467307451
e-ISBN	9781467307475
e-ISBN	9781467307468
DOI	10.1109/ICTEL.2012.6221261
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2012-04-23
Publisher Place	Lebanon
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Support vector machines kernel machines ECG signals Accuracy Heart beat Electrocardiography Multi-class classification Feature extraction Kernel Principal Component Analysis Kernel Support Vector Machines Principal component analysis
Content Type	Text
Resource Type	Article

Sl.	Authority	Responsibilities	Communication Details
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
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
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9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

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