Intelligent classification of electrocardiogram (ECG) signal using extended Kalman Filter (EKF) based neuro fuzzy system.
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.
Meau
Yeong Pong
YP
Department of Biomedical Engineering, Faculty of Engineering, University of Malaya, 50603 Kuala Lumpur, Malaysia.
Ibrahim
Fatimah
F
Narainasamy
Selvanathan A L
SA
Omar
Razali
R
D000465:Algorithms; D004562:Electrocardiography / Q000145:classification*; D017143:Fuzzy Logic; D006801:Humans; D013617:Tachycardia, Supraventricular / Q000175:diagnosis / Q000503:physiopathology