Identification of Premature Ventricular Contraction (PVC) Based on ECG Using Convolutional Neural Network

Premature Ventricular Contraction (PVC) arrhythmia patients are subjected to dangerous heart rhythms that can be chaotic, and possibly result in abrupt death. Therefore, early detection of arrhythmia with high accuracy is extremely important to detect cardiovascular diseases. The classification of h...

Full description

Saved in:
Bibliographic Details
Published inInternational journal of recent technology and engineering Vol. 9; no. 1; pp. 2173 - 2177
Main Authors Kothari, Akanksha, Dighe, Sankeit, Kale, Kedar, Kale, Shubhangi
Format Journal Article
LanguageEnglish
Published 30.05.2020
Online AccessGet full text

Cover

More Information
Summary:Premature Ventricular Contraction (PVC) arrhythmia patients are subjected to dangerous heart rhythms that can be chaotic, and possibly result in abrupt death. Therefore, early detection of arrhythmia with high accuracy is extremely important to detect cardiovascular diseases. The classification of heartbeats based on ECG signals plays a vital role it the field of cardiac sciences to identify arrhythmias. The use of Artificial Neural Networks (ANN) has proven to be the most effective technique for sole agenda of classification. The use of CNN is simple and more noise immune method in comparison to various other techniques. In this paper, a survey of numerous algorithms and classification techniques along with their performance measures are presented. This paper proposes the identification of PVC on the basis of heart beats by using CNN and the results obtained are compared to other traditional approaches.
ISSN:2277-3878
2277-3878
DOI:10.35940/ijrte.A2804.059120