TY  - JOUR
T1  - Performance of ANN Classifier Using HRV Analysis for ECG Database
AU - Deepak Gautam, Desh AU - Upadhyay, K.G. AU - Giri, V.K. 
JO  - Journal of Engineering and Applied Sciences
VL  - 13
IS  - 14
SP  - 5897
EP  - 5903
PY  - 2018
DA  - 2001/08/19
SN  - 1816-949x
DO  - jeasci.2018.5897.5903
UR  - https://makhillpublications.co/view-article.php?doi=jeasci.2018.5897.5903
KW  - biomedical signal processing
KW  -Artificial neural networks
KW  -electrocardiography
KW  -heart rate variability
KW  -pattern recognition
KW  -various
AB  - 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.
ER  -