HRV Analysis and Ventricular Arrhythmia Classification using various Classifiers
Desh Deepak Gautam1, V.K. Giri2, K.G. Upadhyay3
1Desh Deepak Gautam*, Electrical Engineering Department, MMM University of Technology, Gorakhpur, U.P., India.
2Vinod Kumar Giri, Electrical Engineering Department, MMM University of Technology, Gorakhpur, U.P., India.
3Krishn Gopal Upadhyay, Electrical Engineering Department, MMM University of Technology, Gorakhpur, U.P., India.
Manuscript received on November 12, 2019. | Revised Manuscript received on November 25, 2019. | Manuscript published on 30 November, 2019. | PP: 6095-6100 | Volume-8 Issue-4, November 2019. | Retrieval Number: D8730118419/2019©BEIESP | DOI: 10.35940/ijrte.D8730.118419
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Ventricular Arrhythmias are one of the fatal heart diseases, requires timely recognition. This paper deals with the classification of some of the ventricular arrhythmias as Premature Ventricular Contraction (PVC), Left Bundle Branch Block (LBBB) and Right Bundle Brach Block (RBBB) with some Normal (N) samples. A Support Vector Machine (SVM), Random Forest and Artificial Neural Network (ANN) classifier was trained and then tested with the help of online available MIT-BIH Arrhythmia Database. Signal processing, generation of Heart Rate Variability (HRV) signals from the available Electrocardiogram (ECG) signals and training and testing of ANN classifier was done in MATLAB environment, and the training and testing of SVM and Random Forest classifier was done in R project software. The SVM classifier was trained with the linear basis function and then with non-linear kernel based function to have better accuracy.
Keywords: Heart Rate Variability (HRV), Electrocardiogram (ECG), Ventricular Arrhythmias, Support Vector Machine (SVM), Random Forest, Artificial Neural Network (ANN).
Scope of the Article: Application Artificial Intelligence and machine learning in the Field of Network and Database.