Cardiac arrhythmia detection in ECG signals by feature Extraction and support vector machine

• Autorzy: Malik Kumar Gorav , Kumar Yatindra , Panda Manoj

Identyfikatory

DOI

10.15439/2017R63

• Konferencja: The Second International Conference on Research in Intelligent and Computing in Engineering
• Języki publikacji: EN

Abstrakty

EN

Purpose of this work is to develop an automated physiological signal diagnostic tool that can help us to early determination of arrhythmia for proper medical attention. This paper presents a simple automated approach for classification of normal and abnormal ECG based on arrhythmia. The proposed method validated by the data MIT BIH arrhythmia database. The performance in terms of accuracy for clinical decision must be very high. This method uses fourth order wavelet decomposition, wavelet decomposition used for time frequency representation and feature extraction. For classification support vector machine is used for detection kinds of ECG signals

Słowa kluczowe

EN

arrhythmia; support vector machine; wavelet decomposition; pre-processing; feature extraktion; ECG signals

• Wydawca: Polskie Towarzystwo Informatyczne
• Czasopismo: Annals of Computer Science and Information Systems
• Rocznik: 2017
• Tom: Vol. 10
• Strony: 241--244
• Opis fizyczny: Bibliogr. 11 poz., rys tab., wykr.

Twórcy

autor

Malik Kumar Gorav

• Department of Electronic and communication Engineering. F.E.T, Gurukul Kangri University Haridwar, Uttarakhand

autor

Kumar Yatindra

• Department of Electrical Engineering G. B. Pant Engineering College Pauri-Garhwal Uttarakhand

autor

Panda Manoj

• Department of Electrical Engineering G. B. Pant Engineering College Pauri-Garhwal Uttarakhand

Bibliografia

• 1. Bhyri, Channappa, S. T. Hamde, and L. M. Waghmare. "ECG feature extraction and disease diagnosis." Journal of medical engineering & technology 35.6-7 (2011): 354-361.. Goldberger, Ary L., et al. "Physiobank, physiotoolkit, and physionet." Circulation 101.23 (2000): e215-e220.
• 2. Kumar, Yatindra, and Gorav Kumar Malik. "Performance analysis of different filters for power line interface reduction in ECG signal." International Journal of Computer Applications 3.7 (2010): 1-6.
• 3. Singh, Yogendra Narain. "Human recognition using Fisher׳ s discriminant analysis of heartbeat interval features and ECG morphology." Neurocomputing 167 (2015): 322-335.
• 4. Li, Cuiwei, Chongxun Zheng, and Changfeng Tai. "Detection of ECG characteristic points using wavelet transforms." IEEE Transactions on biomedical Engineering 42.1 (1995): 21-28.
• 5. Übeyli, Elif Derya. "Support vector machines for detection of electrocardiographic changes in partial epileptic patients." Engineering Applications of Artificial Intelligence 21.8 (2008): 1196-1203.
• 6. Rai, Hari Mohan, Anurag Trivedi, and Shailja Shukla. "ECG signal processing for abnormalities detection using multi-resolution wavelet transform and Artificial Neural Network classifier." Measurement 46.9 (2013): 3238-3246
• 7. Sherlock, Barry G., and Donald M. Monro. "On the space of orthonormal wavelets." IEEE Transactions on Signal Processing 46.6 (1998): 1716-1720.
• 8. Semwal, Vijay Bhaskar, et al. "An optimized feature selection technique based on incremental feature analysis for bio-metric gait data classification." Multimedia Tools and Applications: 1-19.
• 9. Melgani, Farid, and Yakoub Bazi. "Classification of electrocardiogram signals with support vector machines and particle swarm optimization." IEEE transactions on information technology in biomedicine 12.5 (2008): 667-677.
• 10. Abe, Shigeo. Support vector machines for pattern classification. Vol. 2. London: Springer, 2005.
• 11. Rai, Hari Mohan, Anurag Trivedi, and Shailja Shukla. "ECG signal processing for abnormalities detection using multi-resolution wavelet transform and Artificial Neural Network classifier." Measurement 46.9 (2013): 3238-3246.