Authors : Desh Deepak Gautam, V.K. Giri and K.G. Upadhyay
Abstract: Arrhythmias are the abnormal heartbeats inn which ventricular arrhythmias are a fatal type of them. The timely prediction and classification of this irregularity can help in saving life or human health. In this study, Artificial Neural Network (ANN) classifier has been tested on MIT-BIH database to predict and to classify the ventricular arrhythmias using HRV analysis. HRV or heart rate variability is a low frequency signal showing variations in heart beats andcan be efficiently utilized in the analysis of ECG signals. First, the preprocessing of the available database is done by de-noising and finding the peaks, then the HRV signal is built. ANN is used as a classifier to predict and classify the HRV signals into various arrhythmias.
Desh Deepak Gautam, V.K. Giri and K.G. Upadhyay, 2018. Performance of ANN Classifier Using HRV Analysis for ECG Database. Journal of Engineering and Applied Sciences, 13: 5897-5903.