A Diagnostic System for Detection of Atrial and Ventricular Arrhythmia Episodes from Electrocardiogram
- 46 Downloads
Cardiac arrhythmias, with each passing day, are becoming highly prevalent in modern society. The ever increasing number of patients added to the sheer amount of data that is recorded from a patient during monitoring limits the ability of a medical practitioner to diagnose these problems quickly. Therefore, an intelligent system is required which can produce a reliable diagnosis in a short period of time. In this paper, a new intelligent diagnostic system for detection of atrial arrhythmia (atrial flutter and atrial fibrillation) and ventricular arrhythmia (premature ventricular contractions, ventricular bigeminy, ventricular escape rhythm, ventricular tachycardia and ventricular fibrillation) episodes from Electrocardiogram (ECG) is proposed. This system is based on the non-linear analysis of the variational modes of ECG. Variational mode decomposition is used for decomposition of ECG signal into several modes. Then, the non-linear features namely, the distribution entropy and the sample entropy are evaluated from the modes of ECG. The performance of variational mode distribution entropy (VMDE) and variational mode sample entropy (VMSE) features is assessed using support vector machine (SVM) and adaptive neuro-fuzzy inference system classifiers. Experimental results reveal that, the combination of VMDE and VMSE features, and the radial basis function kernel based multi-class SVM classifier are suitable for detection of arrhythmia episodes from ECG with an average accuracy value of 95.60%.
KeywordsElectrocardiogram Cardiac arrhythmia Variational mode decomposition Sample entropy Distribution entropy Classifiers
- 1.World Health Organisation (2016). WHO factsheet on cardiovascular diseases. Resource document.Google Scholar
- 4.Heart, R. S., Fuster, V., Rydén, L. E., Cannom, D. S., Crijns, H. J., Curtis, A. B., et al. (2006). ACC/AHA/ESC 2006 guidelines for the management of patients with atrial fibrillation–executive summary: A report of the American College of Cardiology/American Heart Association Task Force on Practice Guidelines and the European Society of Cardiology Committee for Practice Guidelines (Writing Committee to Revise the 2001 Guidelines for the Management of Patients With Atrial Fibrillation). Journal of the American College of Cardiology, 48(4), 854.CrossRefGoogle Scholar
- 6.Surawicz, B., & Knilans, T. (2008). Chou’s electrocardiography in clinical practice: Adult and pediatric (6th ed.). Philadelphia: Elsevier Health Sciences.Google Scholar
- 12.Cerutti, S., Mainardi, L. T., Porta, A., & Bianchi, A. M. (1997, September). Analysis of the dynamics of RR interval series for the detection of atrial fibrillation episodes. In Computers in Cardiology 1997 (pp. 77–80). IEEE.Google Scholar
- 23.Goldberger, A. L. (2012). Clinical electrocardiography: A simplified approach (8th ed.). Philadelphia: Elsevier Health Sciences.Google Scholar
- 27.Moody, G. B., & Mark, R. G. (1983). A new method for detecting atrial fibrillation using RR intervals. Computers in Cardiology, 10(1), 227–230.Google Scholar
- 37.Andrew, A. M. (2000). An introduction to support vector machines and other kernel-based learning methods by Nello Christianini and John Shawe-Taylor, Cambridge University Press, Cambridge, 2000, xiii + 189 pp., ISBN 0-521-78019-5 (Hbk,£ 27.50).Google Scholar
- 38.Hsu, C. W., Chang, C. C., & Lin, C. J. (2003). A practical guide to support vector classification. http://www.csie.ntu.edu.tw/~cjlin.
- 40.Buragohain, M., & Mahanta, C. (2006, September). ANFIS Modelling of Nonlinear System Based on Subtractive Clustering and V-fold Technique. In India conference, 2006 annual IEEE (pp. 1–6). IEEE.Google Scholar