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.

[1]  Heitor Silvério Lopes,et al.  Evolutionary training of a neurofuzzy network for detection of P wave of the ECG , 1999, Proceedings Third International Conference on Computational Intelligence and Multimedia Applications. ICCIMA'99 (Cat. No.PR00300).

[2]  Dingli Yu,et al.  Fault-Tolerant Control Based on Adaptive Neural Network , 2001 .

[3]  Stanislaw Osowski,et al.  Fuzzy clustering neural network for classification of ECG beats , 2000, Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks. IJCNN 2000. Neural Computing: New Challenges and Perspectives for the New Millennium.

[4]  C Brohet,et al.  Possibilities of using neural networks for ECG classification. , 1996, Journal of electrocardiology.

[5]  Xin Yao,et al.  Designing Neural Network Ensembles by Minimizing Mutual Information , 2003 .

[6]  J A Kors,et al.  Classification Methods for Computerized Interpretation of the Electrocardiogram , 1990, Methods of Information in Medicine.

[7]  Jeffrey M. Hausdorff,et al.  Physionet: Components of a New Research Resource for Complex Physiologic Signals". Circu-lation Vol , 2000 .