ECG beat recognition using fuzzy hybrid neural network.
This paper presents the application of the fuzzy neural network for electrocardiographic (ECG) beat recognition and classification. The new classification algorithm of the ECG beats, applying the fuzzy hybrid neural network and the features drawn from the higher order statistics has been proposed in the paper. The cumulants of the second, third, and fourth orders have been used for the feature selection. The hybrid fuzzy neural network applied in the solution consists of the fuzzy self-organizing subnetwork connected in cascade with the multilayer perceptron, working as the final classifier. The c-means and Gustafson-Kessel algorithms for the self-organization of the neural network have been applied. The results of experiments of recognition of different types of beats on the basis of the ECG waveforms have confirmed good efficiency of the proposed solution. The investigations show that the method may find practical application in the recognition and classification of different type heart beats.
Osowski
S
S
Institute of the Theory of Electrical Engineering and Electrical Measurements, Warsaw University of Technology, Poland. sto@iem.pw.edu.pl
Linh
T H
TH
D000465:Algorithms; D001698:Biomedical Engineering; D016000:Cluster Analysis; D003198:Computer Simulation; D004562:Electrocardiography / Q000706:statistics & numerical data*; D017143:Fuzzy Logic; D006801:Humans; D008955:Models, Cardiovascular; D016571:Neural Networks, Computer