Abstract
Heart diseases are caused by a multitude of reasons including abnormal propagation of pacing impulses through the specialized cardiac conduction system. Such abnormalities where cardiac rhythm deviates from normal sinus rhythm are termed as arrhythmia. The present contribution concentrates on the application of Multicategory support vector machines (MC-SVMs) for arrhythmia classification. This system of classification comprises of several units including signal preprocessing, wavelet transform (WT) for feature extraction and support vector machine with Gaussian kernel approximation of each arrhythmia class. Training and testing has been done on standard MIT-BIH Arrhythmia database. A systematic and comprehensive evaluation of this algorithm has been conducted where 25 features are being extracted from each arrhythmia beat by wavelet transform, for multi-category classification. Upon implementing MC-SVM techniques one-versus-one, DAGSVM was found to be the most suitable algorithm in this domain. The overall accuracy of classification of the proposed method is 98.50%. This system is flexible, and implements a prototype graphical user interface (GUI) based on MATLAB. The results shown in this paper prove that the method can classify arrhythmia from given ECG data.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Cristianini, N., Shawe-Taylor, J.: An Introduction to Support Vector Machines (and other kernel-based learning methods). Cambridge University Press, Cambridge (2000)
Vapnik, V.N.: The Nature of Statistical Learning Theory. Springer, New York (1995)
Hu, Y.H., Palreddy, S., Tompkins, W.: A patient adaptable ecg beat classifier using a mixture of experts approach. IEEE Trans. Biomed. Eng. 44, 891–900 (1997)
Millet-Roig, J., et al.: Study of frequency and time domain parameters extracted by means of wavelet transform applied to ecg to distinguish vf and other arrhythmias. In: Proc. of. Computers in Cardiology (1998)
Strauss, D.J.: Adapted filter bank in machine learning applications in biomedical signal processing. In: Proc. of Computers in Cardiology (2002)
MIT- BIH arrhythmia database, http://ecg.mit.edu
Minami, K., Nakajima, H., Toyoshima, T.: Real-time discrimination of ventricular tachyarrhythmia with fourier-transform neural network. IEEE Trans. Biomed. Eng. 46 (February 1999)
Daubechies, I.: The wavelet transform, time-frequency localization and signal analysis. IEEE Trans. Inform. Theory 36, 961–1005 (1990)
Kressel, U.: Pairwise classification and support vector machines. In: Schölkopf, B., Burges, C.J.C., Smola, A.J. (eds.) Advances in Kernel Methods - Support Vector Learning, MIT Press, Cambridge (1999)
Platt, J.C.: Fast training of support vector machines using sequential minimal optimization. In: Schölkopf, B., Burges, C.J.C., Smola, A.J. (eds.) Advances in Kernel Methods - Support Vector Learning. ch. 12, pp. 185–208. MIT Press, Cambridge (1999)
Chang, C.-C., Lin, C.-J.: Libsvm a library for support vector machines, http://www.csie.ntu.edu.tw/cjlin/libsvm
Mukherjee, S.: Classifying microarray data using support vector machines. In: Understanding And Using Microarray Analysis Techniques: A Practical Guide, Kluwer Academic Publishers, Boston (2003)
Vapnik, V.N.: Statistical Learning Theory. Wiley Series on Adaptive and Learning Systems for Signal Processing, Communications and Control. Wiley, New York (1998)
Friedman, J.: Another approach to polychotomous classification. tech. rep., Stanford Univeristy (1996)
Millet-Roig, J.: Support vector machine for arrhythmia discrimination with wavelet transform feature selection. In: Proc. of Computers in Cardiology (2000)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2004 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Khadtare, M.S., Sahambi, J.S. (2004). ECG Arrhythmia Analysis by Multicategory Support Vector Machine. In: Manandhar, S., Austin, J., Desai, U., Oyanagi, Y., Talukder, A.K. (eds) Applied Computing. AACC 2004. Lecture Notes in Computer Science, vol 3285. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30176-9_13
Download citation
DOI: https://doi.org/10.1007/978-3-540-30176-9_13
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-23659-7
Online ISBN: 978-3-540-30176-9
eBook Packages: Springer Book Archive