Home   >   CSC-OpenAccess Library   >    Manuscript Information
Full Text Available

This is an Open Access publication published under CSC-OpenAccess Policy.
Publications from CSC-OpenAccess Library are being accessed from over 74 countries worldwide.
Classification of Cardiac Arrhythmia using WT, HRV, and Fuzzy C-Means Clustering
Pages - 101 - 109     |    Revised - 01-07-2011     |    Published - 05-08-2011
Volume - 5   Issue - 3    |    Publication Date - July / August 2011  Table of Contents
Classification of ECG Arrhythmias, Fuzzy C-Means Clustering, Wavelet Transform
The classification of the electrocardiogram registration into different pathologies disease devises is a complex pattern recognition task. In this paper, we propose a generic feature extraction for classification of ECG arrhythmias using a fuzzy c-means (FCM) clustering and Heart Rate variability (HRV). The traditional methods of diagnosis and classification present some inconveniences; seen that the precision of credit note one diagnosis exact depends on the cardiologist experience and the rate concentration. Due to the high mortality rate of heart diseases, early detection and precise discrimination of ECG arrhythmia is essential for the treatment of patients. During the recording of ECG signal, different forms of noise can be superimposed in the useful signal. The pre-treatment of ECG imposes the suppression of these perturbation signals. The row date is preprocessed, normalized and then data points are clustered using FCM technique. In this work, four different structures, FCM-HRV, PCM-HRV, FCMC-HRV and FPCM-HRV are formed by using heart rate variability technique and fuzzy c-means clustering. In addition, FCM-HRV is the new method proposed for classification of ECG. This paper presents a comparative study of the classification accuracy of ECG signals by using these four structures for computationally efficient diagnosis. The ECG signals taken from MIT-BIH ECG database are used in training to classify 4 different arrhythmias (Atrial Fibrillation Termination). All of the structures are tested by using the same ECG records. The test results suggest that FCMC-HRV structure can generalize better and is faster than the other structures.
CITED BY (3)  
1 Jambukia, S. H., Dabhi, V. K., & Prajapati, H. B. (2015, March). Classification of ECG signals using machine learning techniques: A survey. In Computer Engineering and Applications (ICACEA), 2015 International Conference on Advances in (pp. 714-721). IEEE.
2 Shih, H. H. (2014). Cardiac Arrhythmia Classification and ECG Synthesis Based on Dynamic models.
3 Mora, L. A., & Amaya, J. E. (2012, October). Proposal of asymmetric multi-classifier of arrhythmias. In Informatica (CLEI), 2012 XXXVIII Conferencia Latinoamericana En (pp. 1-7). IEEE.
1 Google Scholar 
2 CiteSeerX 
3 refSeek 
4 Scribd 
5 SlideShare 
6 PdfSR 
1 Owis, M. I., Youssef, A.-B. M., & Kadah, Y. M., “Characterization of ECG signals based on blind source separation,” Medical and Biological Engineering and Computing, vol. 40, pp. 557–564, 2002.
2 Rahime Ceylan a,*, Yuksel Ozbay a, Bekir Karlik b, 2009. A novel approach for classification of ECG arrhythmias: Type-2 fuzzy clustering neural network. Expert Systems with Applications,6721-6726
3 Yu, S.-N., & Chou, K.-T., “Integration of independent component analysis and neural networks for ECG beat classification,” Expert Systems with Applications, vol. 34, pp. 2841-2846, 2008.
4 Ceylan, R., & Ozbay, Y. (2007). Comparison of FCM, PCA and WT techniques for classification ECG arrhythmias using artificial neural network. Expert Systems with Applications, 33, 286–295.
5 Guler, I. & Ubeyli, E. D., “ECG beat classifier designed by combined neural network model,” Pattern Recognition, vol. 38, pp. 199-208, 2005.
6 M. I. Owis, A. B. M. Youssef and Y. M. Kadah. “Characterisation of electrocardiogram signals based on blind source separation” ,Medical & Biological Engineering & Computing, Cairo, Egypt 2002.
7 Dipti Patra and Smita Pradhan, “Integration of FCM, PCA and Neural networks for classification of ECG Arrhythmias,” IAENG International Journal of Computer Science, 36:3, IJCS_36_3_05 , 01 August. 2009.
8 E. M. Tamil, N. H. Kamarudin, R. Salleh, M. Yamani Idna Idris, M. N. Noorzaily, and A. M. Tamil, (2008) Heartbeat electrocardiogram (ECG) signal feature extraction using dis-crete wavelet transforms (DWT), in Proceedings of CSPA, 1112–1117
9 A. Dallali, A. Kachouri, and M. Samet, “A Comparison of Wavelets_Based to Guaranteeing ECG Compression quality”. World Academy of Science, Engineering and Technology 55 2009
10 Yu, S.-N., & Chou, K.-T., “Integration of independent component analysis and neural networks for ECG beat classification,” Expert Systems with Applications, vol. 34, pp. 2841-2846, 2008.
11 Srinivasca K G, Amrinder Singh, A O Thomas, Venugopal K R and L M Patnaik,” Generic Feature Extraction for Classification using Fuzzy C – Means Clustering”, 0- 7803 – 9588 – 3/05/$20.00 ©2005 IEEE.
12 A. Kachouri, M. Ben Messaoud and A. Dallali: ‘Wavelet based on Electrocardiogram Signal Analysis for Classification and Diabnosis By Neural Networks’, SSD’03 March 26-28, 2003 – Sousse, Tunisia
13 Yu, S. N., & Chen, Y. H. (2007). Electrocardiogram beat classification based on wavelet transformation and probabilistic neural network. Pattern Recognition Letters, 28, 1142–1150
14 B. Anuradha and V.C. Veera Reddy June 2008. ANN For Classification Of Cardiac Arrhythmias. ARPN Journal of Engineering and Applied Sciences. Vol. 3, NO. 3, June 2008
15 B.U. Kohler, C. Hennig and R. Orglmeister, “The principles of software QRS detection”, IEEE Engineering in Medicine and Biology Magazine, vol. 21, no. 1, pp. 42-57, Jan.-Feb. 2002.
16 S. Z. Mahmoodabadi, A. Ahmadian, and M. D. Abolhasani, “ECG Feature Extraction using Daubechies Wavelets”, Proceedings of the fifth IASTED International conference on Visualization, Imaging and Image Processing, pp. 343-348, 2005.
17 http://www.physionet.org/physiobank/database/mitdb/
18 http://www.physionet.org/physiobank/database/slpdb/
LETI - Tunisia