Browse > Article
http://dx.doi.org/10.9718/JBER.2009.30.4.294

An Adaptive Classification Algorithm of Premature Ventricular Beat With Optimization of Wavelet Parameterization  

Kim, Jin-Kwon (Department of Electrical and Electronic Engineering, Yonsei University)
Kang, Dae-Hoon (Department of Electrical and Electronic Engineering, Yonsei University)
Lee, Myoung-Ho (Department of Electrical and Electronic Engineering, Yonsei University)
Publication Information
Journal of Biomedical Engineering Research / v.30, no.4, 2009 , pp. 294-305 More about this Journal
Abstract
The bio signals essentially have different characteristics in each person. And the main purpose of automatic diagnosis algorithm based on bio signals focuses on discriminating differences of abnormal state from personal differences. In this paper, we propose automatic ECG diagnosis algorithm which discriminates normal heart beats from premature ventricular contraction using optimization of wavelet parameterization to solve that problem. The proposed algorithm optimizes wavelet parameter to let energy of signal be concentrated on specific scale band. We can reduce the personal differences and consequently highlight the differences coming from arrhythmia via this process. The proposed algorithm using ELM as a classifier show high discrimination performance between normal beat and PVC. From the experimental results on MIT-BIH arrhythmia database the performances of the proposed algorithm are 98.1% in accuracy, 93.0% in sensitivity, 96.4% in positive predictivity, and 0.8% in false positive rate. This results are similar or higher then results of existing researches in spite of small human intervention.
Keywords
Premature Ventricular Beat; Wavelet Parameterization; arrhythmia classification; ECG;
Citations & Related Records
연도 인용수 순위
  • Reference
1 J.S Sahambi, S.N. Tandon and R.K.P. Bhatt, 'Using wavelet transforms for ECG characterization. An on-line digital signal processing system', Engineering in Medicine and Biology Magazine, IEEE, Volume 16, Issue 1, Page(s):77-83, 1997   DOI   ScienceOn
2 Myoungho Lee, Se Dong Min, Hang Sik Shin, Byung Woo Lee, Jin Kwon Kim, The e-Health Landscape: Current Status and Future Prospects in Korea, TELEMEDICINE JOURNAL AND E-HEALTH, Volume 15, Issue 4, pp. 362-369 2009   DOI   ScienceOn
3 Jinkwon Kim, Hangsik Shin, Yonwook Lee, Myoungho Lee, 'Algorithm for Classifying Arrhythmia using Extreme Learning Machine and Principal Component Analysis', 2007. EMBS, pp. 3257-3260, 22-26 Aug. 2007
4 L Biel, O Pettersson, L Philipson, P Wide, 'ECG analysis: a new approach in human identification', Instrumentation and Measurement, IEEE Trans., Volume 50, Issue 3, pp. 808-812, 2001   DOI   ScienceOn
5 Brechet, L., Lucas, M.-F., Doncarli, C., Farina, D., 'Compression of Biomedical Signals With Mother Wavelet Optimization and Best-Basis Wavelet Packet Selection', Biomedical Engineering, IEEE Trans., Volume 54, Issue.12, pp. 2186-2192, 2007   DOI   ScienceOn
6 Jinkwon Kim, Byoungwoo Lee, Myoungho Lee, 'Optimization on arrhythmia classification algorithm using wavelet parameterization', 2008 Conference on Information and Control System, Gangwon, Korea, Oct. 31, 2008
7 Guang-Bin Huang, Qin-Yu Zhu and Chee-Kheong Siew, 'Extreme learning machine: Theory and applications' Neurocomputing, Volume 70, Issues 1-3, Pages 489-501, December 2006   DOI   ScienceOn
8 P.L. Barlett, 'The sample complexity of pattern classification with neural networks: The size of the weights is more important than the size of the network', Information Theory, IEEE Trans.,Volume 44, no. 2, pp. 525-536, 1998   DOI   ScienceOn
9 M.H.Kadbi, J.Hashemi, H.R.Mohseni and A.Maghsoudi, 'Classification of ECG Arrhythmias Based on Statistical and Time-Frequency Features', Advances in Medical, Signal and Information Processing, MEDSIP, 2006
10 K. Minami, H. Nakajima, and T. Toyoshima, 'Real-time discrimination of ventricular tachyarrhythmia with Fourier-transform neural network,' Biomedical Engineering, IEEE Trans., Volume 46, No. 2, pp. 179-185 1999
11 I.Christov, I.Jekova, and G.Bortolan, 'Premature ventricular contraction classification by the kth nearest-neighbours rule', Physiol. Meas., Vol. 26, pp. 123-130, 2005   DOI   ScienceOn
12 Burrus, C.S., Gopinath, R.A., and Guo, H., Introduction to wavelets and wavelet transforms, Prentice Hall, 1997, pp.53-66
13 Marie-Francoise Lucas, Adrien Gaufriau, Sylvain Pascual, Christian Doncarli and Dario Farina, 'Multi-channel surface EMG classification using support vector machines and signalbased wavelet optimization', Biomedical Signal Processing and Control, Volume 3, Issue 2, pp. 169-174, 2008   DOI   ScienceOn
14 Philip de Chazal, Oapos, M. Dwyer, R.B. Reilly, 'Automatic classification of heartbeats using ECG morphology and heartbeat interval features', Biomedical Engineering, IEEE Trans., Volume 51, Issue 7, pp. 1196-1206, 2004   DOI   ScienceOn
15 Philip de Chazal, RB Reilly, 'A Patient-Adapting Heartbeat Classifier Using ECG Morphology and Heartbeat Interval Features', Biomedical Engineering, IEEE Trans., Volume 53, No.12, pp. 2535-2543, 2006   DOI   ScienceOn
16 Youngbum Lee, Myoungho Lee, Development of an Integrated Module Using a Wireless Accelerometer and ECG Sensor to Monitor Activities of Daily Living, TELEMEDICINE JOURNAL AND E-HEALTH, Volume 14, Issue 6, pp. 580-586 2008   DOI   ScienceOn
17 S. Osowski, , and T. H. Linh, 'ECG beat recognition using fuzzy hybrid neural network,' IEEE Trans. Biomed. Eng., Vol. 48, pp. 1265-1271, 2001   DOI   ScienceOn