ECG beat recognition using fuzzy hybrid neural network

• Published in: IEEE transactions on biomedical engineering Vol. 48; no. 11; pp. 1265 - 1271
• Main Authors: Osowski, S., Tran Hoai Linh
• Format: Journal Article
• Language: English
• Published: New York, NY IEEE 01.11.2001; Institute of Electrical and Electronics Engineers; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Biological and medical sciences; Biomedical Engineering; Cardiac dysrhythmias; Cardiology. Vascular system; Classification algorithms; Cluster Analysis; Computer Simulation; Electric variables measurement; Electrocardiography; Electrocardiography - statistics & numerical data; Electrocardiography. Vectocardiography; Electrodiagnosis. Electric activity recording; Fuzzy Logic; Fuzzy neural networks; Fuzzy sets; Heart; Heart beat; Helium; Higher order statistics; Humans; Investigative techniques, diagnostic techniques (general aspects); Medical sciences; Models, Cardiovascular; Morphology; Multilayer neural networks; Multilayer perceptrons; Neural networks; Neural Networks (Computer); Pattern recognition; Self organizing maps; Human; Evaluation; Arrhythmia; Cardiovascular disease; Pattern recognition; Neural network; Heart rate; Electrodiagnosis; Sensitivity; Fuzzy algorithm; Heart disease; Electrocardiography; Diagnostic aid; Signal analysis
• ISSN: 0018-9294; 1558-2531
• DOI: 10.1109/10.959322

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.