Intelligent classification of electrocardiogram (ECG) signal using extended Kalman Filter (EKF) based neuro fuzzy system

Meau, Y.P. and Ibrahim, F. and Narainasamy, S.A.L. and Omar, R. (2006) Intelligent classification of electrocardiogram (ECG) signal using extended Kalman Filter (EKF) based neuro fuzzy system. Computer Methods and Programs in Biomedicine, 82 (2). pp. 157-168. ISSN 0169-2607

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Abstract

This study presents the development of a hybrid system consisting of an ensemble of Extended Kalman Filter (EKF) based Multi Layer Perceptron Network (MLPN) and a one-pass learning Fuzzy Inference System using Look-up Table Scheme for the recognition of electrocardiogram (ECG) signals. This system can distinguish various types of abnormal ECG signals such as Ventricular Premature Cycle (VPC), T wave inversion (TINV), ST segment depression (STDP), and Supraventricular Tachycardia (SVT) from normal sinus rhythm (NSR) ECG signal. © 2006 Elsevier Ireland Ltd. All rights reserved.

Item Type: Article
Additional Information: Meau, Yeong Pong Ibrahim, Fatimah Narainasamy, Selvanathan A L Omar, Razali eng Research Support, Non-U.S. Gov't Ireland 2006/04/28 09:00 Comput Methods Programs Biomed. 2006 May;82(2):157-68. Epub 2006 Apr 25.
Uncontrolled Keywords: ECG, Kalman Filter, Neural network, Neuro fuzzy, Artificial intelligence, Fuzzy sets, Inference engines, Kalman filtering, Learning systems, Neural networks, Electrocardiogram (ECG) signals, Fuzzy Inference System, Supraventricular Tachycardia (SVT), Electrocardiography, article, clinical article, electrocardiogram, filter, human, instrumentation, signal transduction, sinus rhythm, ST segment depression, supraventricular tachycardia, T wave inversion, Algorithms, Fuzzy Logic, Humans, Tachycardia, Supraventricular
Subjects: T Technology > T Technology (General)
T Technology > TA Engineering (General). Civil engineering (General)
Divisions: Faculty of Engineering
Depositing User: Mr Jenal S
Date Deposited: 07 Apr 2014 02:49
Last Modified: 01 Nov 2017 05:45
URI: http://eprints.um.edu.my/id/eprint/9299

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