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Performance Evaluation of Boosting Techniques for Cardiac Arrhythmia Prediction
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 57 - Number 17 |
| Year of Publication: 2012 |
| Authors: V. S. R. Kumari, P. Rajesh Kumar |
10.5120/9206-3740
|
V. S. R. Kumari, P. Rajesh Kumar . Performance Evaluation of Boosting Techniques for Cardiac Arrhythmia Prediction. International Journal of Computer Applications. 57, 17 ( November 2012), 18-22. DOI=10.5120/9206-3740
Abstract
Cardiac Arrhythmia is assessed using Electrocardiogram (ECG). Different types of arrhythmia are determined by accurate detection of beats leading to diagnosis of heart disease. Visual inspection of ECG for arrhythmia is tedious and time consuming process. With the advent of image processing techniques, automatic assessment of arrhythmia is widely studied. Various algorithms were developed for detection and classification of ECG signals. This paper investigates ECG classification method for arrhythmic beat classification based on RR interval. The methodology is based on extraction of RR interval of the beat using Symlet on ECG data. The extracted RR data are used as feature for classification. The beats are classified using boosting algorithm. MIT-BIH arrhythmia database was used for evaluating the classification efficiency.
References
- K. Polat and S. Günes, Detection of ECG arrhythmia using a differential expert system approach based on principal component analysis and least square support vector machine, Applied Mathematics and Computation, pp. 898–906, 2007.
- K. Minami, H. Nakajima, and T. Toyoshima, "Real-Time discrimination of ventricular tachyarrhythmia with Fourier-transform neural network," IEEE Trans. Biomed. Eng. , vol. 46, no. 2, pp. 179–185, Feb. 1999.
- S. C. Saxena, V. Kumar, and S. T. Hamde, Feature extraction from ECG signals using wavelet transforms for disease diagnostics, Int. J. Syst. Sci. 33 (13), pp. 1073–1085, 2002.
- S. Osowski, T. H. Linh, and T. Markiewicz, "Support vector machinebased expert system for reliable heart beat recognition," IEEE Trans. Biomed. Eng. , vol. 51, no. 4, pp. 582–589, Apr. 2004.
- P. E. McSharry, G. D. Clifford, L. Terassenko and L. A. Smith, "A dynamical model for generating synthetic electrocardiogram signals," IEEE Trans. Biomedical Engineering, Vol. 50, Issue 3, pp. 289–294, Mar. 2003.
- Al-Fahoum, A. S. , Howitt, I. , 1999," Combined wavelet transformation and radial basis neural networks for classifying life-threatening cardiac arrhythmias", Med. Biol. Eng. Comput. (37), 566–573.
- L. Senhadji, G. Carrault, J. J. Bellanger, and G. Passariello, "Comparing wavelet transforms for recognizing cardiac patterns," IEEE Eng. Med. Biol. Mag. , vol. 14, pp. 167–173, Mar. –Apr. 1995.
- T. H. Yeap, F. Johnson, and M. Rachniowski, "ECG beat classification by a neural network," in Proc. Annu. Int. Conf. IEEE Engineering Medicine and Biology Soc. , 1990, pp. 1457–1458.
- Y. H. Hu,W. J. Tompkins, J. L. Urrusti, and V. X. Afonso, "Applications of artificial neural networks for ECG signal detection and classification," J. Electrocardiol. , vol. 26, pp. 66–73, 1993.
- S. Osowski and T. L. Linh, "ECG beat recognition using fuzzy hybrid neural network," IEEE Trans. Biomed. Eng. , vol. 48, pp. 1265–1271, Nov. 2001.
- Y. H. Hu, S. Palreddy, and W. J. Tompkins, "A patient-adaptable ECG beat classifier using a mixture of experts approach," IEEE Trans. Biomed. Eng. , vol. 44, pp. 891–900, Sept. 1997.
- M. Lagerholm, C. Peterson, G. Braccini, L. Edenbrandt, and L. Sornmo, "Clustering ECG complexes using hermite functions and self-organizing maps," IEEE Trans. Biomed. Eng. , vol. 47, pp. 838–848, July 2000.
- M. G. Tsipouras, D. I. Fotiadis, and D. Sideris, "An arrhythmia classi?cation system based on the RR interval signal," Artif. Intell. Med. , vol. 33, pp. 237–250, 2005.
- Ahmad Khoureich Ka, "ECG Beats Classification using Waveform similarity and RR Interval", Arxiv preprint arXiv: 1101. 1836, 2011.
- Mohamed Ezzeldin A. Bashir , Makki Akasha, D. G. Lee, Min Yi, K. H. Ryu , E. J. Bae, M. Cho, and C. Yoo, "Nested Ensemble Technique for Excellence Real Time Cardiac Health Monitoring", BioComp, lasvegas USA, 2010.
- Vinay U. Kale & Nikkoo N. Khalsa "Performance Evaluation of Various Wavelets for Image Compression of Natural and Artificial Images" International Journal of Computer Science & Communication,Vol. 1, No. 1, January-June 2010, pp. 179-184.
- Freund Y, Schapire R. A decision-theoretical generalization of on-line learning and an application to boosting. J Computer Syst Sci 1997;55:119-139.
- Friedman J, Hastie T, Tibshirani R. Additive logistic regression: a statistical view of boosting. Ann Stat 2000;28:337-407.
- Mitchell, T. M. (1997). Machine Learning. McGraw-Hill, New York.
- Quinlan, J. R. (1987). Simplifying decision trees. In B. Gaines and J. Boose, editors, Knowledge Acquisition for Knowledge-Based Systems, pages 239–252. Academic Press, London.
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