Open AccessAn Approach of Neural Network For Electrocardiogram Classification
Author(s) -
Mayank Kumar Gautam,
V. K. Giri
Publication year - 2020
Publication title -
aptikom journal on computer science and information technologies
Language(s) - English
Resource type - Journals
eISSN - 2528-2425
pISSN - 2528-2417
DOI - 10.34306/csit.v1i3.57
Subject(s) - pattern recognition (psychology) , artificial intelligence , computer science , artificial neural network , feature extraction , electrocardiography , classifier (uml) , signal (programming language) , cardiology , medicine , programming language
ECG is basically the graphical representation of the electrical activity of cardiac muscles duringcontraction and release stages. It helps in determination of the cardiac arrhythmias in a well manner. Due to thisearly detection of arrhythmias can be done properly. In other words we can say that the bio-potentials generated bythe cardiac muscles results in an electrical signal called Electro-cardiogram (ECG). It acts as a vital physiologicalparameter, which is being used exclusively to know the state of the cardiac patients. Feature extraction of ECG playsa vital role in the manual as well as automatic analysis of ECG. In this paper the study of the concept of patternrecognition of ECG is done. It refers to the classification of data patterns and characterizing them into classes ofpredefined set. The analysis ECG signal falls under the application of pattern recognition. The ECG signal generatedwaveform gives almost all information about activity of the heart. The ECG signal feature extraction parameters suchas spectral entropy, Poincare plot and Lyapunov exponent are used for study in this paper .This paper also includesartificial neural network as a classifier for identifying the abnormalities of heart disease.