Cart (Loading....) | Create Account
Close category search window

Artificial neural network based automatic cardiac abnormalities classification

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

The purchase and pricing options are temporarily unavailable. Please try again later.
3 Author(s)
Issac Niwas, S. ; Dept. of Electr. & Comput. Eng., Mepco Schlenk Eng. Coll., Tamil Nadu, India ; Shantha Selva Kumari, R. ; Sadasivam, V.

Automatic detection and classification of cardiac arrhythmias from a limited number of ECG signals is of considerable importance in critical care or operating room patient monitoring. We propose a method to accurately classify the heartbeat of ECG signals through the artificial neural networks (ANN). Feature sets are based on Heartbeat intervals, RR intervals and Spectral entropy of the ECG signal. The ability of properly trained artificial neural networks to correctly classify and recognize patterns makes them particularly suitable for use in an expert system that aids in the interpretation of ECG signals. In the present work the ECG data is taken from standard MIT-BIH arrhythmia database. The proposed method is capable of distinguishing the normal beat and 9 different arrhythmias. The overall accuracy of classification of the proposed approach is 99.02%. The results of the analysis are found to be more accurate than the other existing methods. Detection and classification of cardiac signals is important for diagnosis of cardiac abnormalities and hence any automated processing of the ECG that assists this process would be of assistance and is the focus of this paper.

Published in:

Computational Intelligence and Multimedia Applications, 2005. Sixth International Conference on

Date of Conference:

16-18 Aug. 2005

Need Help?

IEEE Advancing Technology for Humanity About IEEE Xplore | Contact | Help | Terms of Use | Nondiscrimination Policy | Site Map | Privacy & Opting Out of Cookies

A not-for-profit organization, IEEE is the world's largest professional association for the advancement of technology.
© Copyright 2014 IEEE - All rights reserved. Use of this web site signifies your agreement to the terms and conditions.