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

Yeong Pong Meau; Fatimah Ibrahim; Selvanathan A L Narainasamy; Razali Omar

doi:10.1016/j.cmpb.2006.03.003

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

Comput Methods Programs Biomed. 2006 May;82(2):157-68. doi: 10.1016/j.cmpb.2006.03.003. Epub 2006 Apr 25.

Authors

Yeong Pong Meau¹, Fatimah Ibrahim, Selvanathan A L Narainasamy, Razali Omar

Affiliation

¹ Department of Biomedical Engineering, Faculty of Engineering, University of Malaya, 50603 Kuala Lumpur, Malaysia.

PMID: 16638620
DOI: 10.1016/j.cmpb.2006.03.003

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.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Algorithms
Electrocardiography / classification*
Fuzzy Logic*
Humans
Tachycardia, Supraventricular / diagnosis
Tachycardia, Supraventricular / physiopathology