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 c...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
2006
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| Subjects: | |
| Online Access: | http://eprints.um.edu.my/9299/1/Intelligent_classification_of_electrocardiogram_%28ECG%29_signal_using_extended_Kalman_Filter_%28EKF%29_based_neuro_fuzzy_system.pdf |
| Summary: | 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. |
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