Classification of Tachycardia, Bradycardia and Premature Ventricular Arrhythmias Using Support Vector Machine
Keywords:
Electrocardiogram Signal classification, Support Vector Machine, Tachycardia, Bradycardia, Premature Ventricular Contractions.
Abstract
An electrocardiogram (ECG) signal is one of the most important non-invasive tools for diagnosing cardiac arrhythmias. This article is about the automatic classification of arrhythmias and premature ventricular arrhythmias (PVC). QRS detection is performed using the Pan Tompkins algorithm. Heart beat is identified by three consecutive RR characteristics related to the current heartbeat, the previous heartbeat, and the next heartbeat. Classification is performed based on the percentage of the desired heart beat in order to identify patients with significant risk factors. Multi-class support vector machine (SVM) with one-to-one (OAO) approach is used to classify type of arrhythmias. The measured data were extracted from the MIT-BIH database. The proposed method has an accuracy of 97.92 and 95.83 for heart beat arrhythmia, PVC arrhythmia detection and a total accuracy of 96.87, respectively.
References
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[4] M. Engin, “ECG beat classification using neuro-fuzzy network,” Pattern Recognition Letters, Vol. 25, No. 15, pp. 1715-1722, 2004.
[5] قبادی محبی، سحر، راحتی قوچانی، سعید و گلمکانی، عباس، تشخیص و طبقهبندی آریتمیهای قلبی با استفاده از روش SVM، سیزدهمین کنفرانس دانشجویی مهندسی برق ایران، تهران، 1389.
[6] C. Ye, M.T. Coimbra and B.V. Kumar, “Arrhythmia detection and classification using morphological and dynamic features of ECG signals,” Proc. Annual Int. Conf. of the IEEE Engineering in Medicine and Biology, pp. 1918-1921, 2010.
[7] J. Pan, W.J. Tompkins, “A Real time QRS detection algorithm,” IEEE Trans. on Biomedical Engineering, Vol. BME-32, No.3, 1985.
[8] W.J. Tompkins, Biomedical digital signal processing – C-language examples and laboratory experiments for the IBM PC, Prentice Hall of India Pvt. Ltd, 2008.
[9] M.G. Tsipouras, D.I. Fotiadis and D. Sideris, “Arrhythmia classification using the RR – interval duration signal,” Computers in Cardiology, Vol. 29, pp. 485-488, 2002.
[10] M.G. Tsipouras, D.I. Fotiadis and D. Sideris, “An Arrhythmia classification system based on the RR – interval signal,” Artificial Intelligence in Medicine, Vol. 33, pp. 237-250, 2005.
[11] Massachusetts Institute of Technology, MIT-BIH arrhythmia database, http://www.physionet.org/physiobank/ database/mitdb.
[12] D. Boswell, Introduction to support vector machines, Department of Computer Science and Engineering University of California San Diego, 2002.
[13] G.G. Sanchez, Examination of the applicability of Support Vector Machines in the context of ischaemia detection, Diploma Thesis, University of Dresden, 2010.
[14] J. Milgram, M. Cheriet and R. Sabourin, “One Against One” or “One Against All”: which one is better for handwriting recognition with SVMs?,”, 2006.
[15] V. Kalidas and L.S. Tamil, “Enhancing accuracy of arrhythmia classification by combining logical and machine learning techniques,” Proc. IEEE Computing in Cardiology Conf. (CinC), pp. 733-736, 2015.
[16] Q. Li, C. Rajagopalan and G.D. Clifford, “Ventricular fibrillation and tachycardia classification using a machine learning approach,” IEEE Trans. on Biomedical Engineering, Vol. 61, No. 6, pp. 1607-1613, 2013.
[17] Y. Kaya and H. Pehlivan, “Classification of premature ventricular contraction in ECG,” International Journal on Advanced Computer Science Applications. Vol. 6, No. 7, pp. 34-40, 2015.
[18] N. Alajlan, Y. Bazi, F. Melgani, S. Malek and M.A. Bencherif, “Detection of premature ventricular contraction arrhythmias in electrocardiogram signals with kernel methods,” Signal, Image and Video Processing, Vol. 8, No. 5, pp. 931-942, 2014.
[2] S. Faziludeen and P.V. Sabiq, “ECG beat classification using wavelets and SVM,” Proc. IEEE Conf. on Information & Communication Technologies, pp. 815-818, 2013.
[3] W. Jatmiko, W.P. Nulad, I.E. Matul, I.M. Setiawan and P. Mursanto. “Heart beat classification using wavelet feature based on neural network,” WSEAS Trans. on Systems, Vol. 10, No. 1, pp. 17-26, 2011.
[4] M. Engin, “ECG beat classification using neuro-fuzzy network,” Pattern Recognition Letters, Vol. 25, No. 15, pp. 1715-1722, 2004.
[5] قبادی محبی، سحر، راحتی قوچانی، سعید و گلمکانی، عباس، تشخیص و طبقهبندی آریتمیهای قلبی با استفاده از روش SVM، سیزدهمین کنفرانس دانشجویی مهندسی برق ایران، تهران، 1389.
[6] C. Ye, M.T. Coimbra and B.V. Kumar, “Arrhythmia detection and classification using morphological and dynamic features of ECG signals,” Proc. Annual Int. Conf. of the IEEE Engineering in Medicine and Biology, pp. 1918-1921, 2010.
[7] J. Pan, W.J. Tompkins, “A Real time QRS detection algorithm,” IEEE Trans. on Biomedical Engineering, Vol. BME-32, No.3, 1985.
[8] W.J. Tompkins, Biomedical digital signal processing – C-language examples and laboratory experiments for the IBM PC, Prentice Hall of India Pvt. Ltd, 2008.
[9] M.G. Tsipouras, D.I. Fotiadis and D. Sideris, “Arrhythmia classification using the RR – interval duration signal,” Computers in Cardiology, Vol. 29, pp. 485-488, 2002.
[10] M.G. Tsipouras, D.I. Fotiadis and D. Sideris, “An Arrhythmia classification system based on the RR – interval signal,” Artificial Intelligence in Medicine, Vol. 33, pp. 237-250, 2005.
[11] Massachusetts Institute of Technology, MIT-BIH arrhythmia database, http://www.physionet.org/physiobank/ database/mitdb.
[12] D. Boswell, Introduction to support vector machines, Department of Computer Science and Engineering University of California San Diego, 2002.
[13] G.G. Sanchez, Examination of the applicability of Support Vector Machines in the context of ischaemia detection, Diploma Thesis, University of Dresden, 2010.
[14] J. Milgram, M. Cheriet and R. Sabourin, “One Against One” or “One Against All”: which one is better for handwriting recognition with SVMs?,”, 2006.
[15] V. Kalidas and L.S. Tamil, “Enhancing accuracy of arrhythmia classification by combining logical and machine learning techniques,” Proc. IEEE Computing in Cardiology Conf. (CinC), pp. 733-736, 2015.
[16] Q. Li, C. Rajagopalan and G.D. Clifford, “Ventricular fibrillation and tachycardia classification using a machine learning approach,” IEEE Trans. on Biomedical Engineering, Vol. 61, No. 6, pp. 1607-1613, 2013.
[17] Y. Kaya and H. Pehlivan, “Classification of premature ventricular contraction in ECG,” International Journal on Advanced Computer Science Applications. Vol. 6, No. 7, pp. 34-40, 2015.
[18] N. Alajlan, Y. Bazi, F. Melgani, S. Malek and M.A. Bencherif, “Detection of premature ventricular contraction arrhythmias in electrocardiogram signals with kernel methods,” Signal, Image and Video Processing, Vol. 8, No. 5, pp. 931-942, 2014.
Published
2021-03-01
How to Cite
Khiadani, N. (2021). Classification of Tachycardia, Bradycardia and Premature Ventricular Arrhythmias Using Support Vector Machine. Majlesi Journal of Mechatronic Systems, 10(1), 17-21. Retrieved from https://ms.majlesi.info/index.php/ms/article/view/473
Section
Articles
