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