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Journal of Medical Systems
Published in: Journal of Medical Systems 2/2011

01-04-2011 | Original Paper

Fuzzy Clustered Probabilistic and Multi Layered Feed Forward Neural Networks for Electrocardiogram Arrhythmia Classification

Authors: Hassan Hamsa Haseena, Abraham T. Mathew, Joseph K. Paul

Published in: Journal of Medical Systems | Issue 2/2011

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Abstract

The role of electrocardiogram (ECG) as a noninvasive technique for detecting and diagnosing cardiac problems cannot be overemphasized. This paper introduces a fuzzy C-mean (FCM) clustered probabilistic neural network (PNN) for the discrimination of eight types of ECG beats. The performance has been compared with FCM clustered multi layered feed forward network (MLFFN) trained with back propagation algorithm. Important parameters are extracted from each ECG beat and feature reduction has been carried out using FCM clustering. The cluster centers form the input of neural network classifiers. The extensive analysis using the MIT-BIH arrhythmia database has shown an average classification accuracy of 97.54% with FCM clustered MLFFN and 99.58% with FCM clustered PNN. Fuzzy clustering improves the classification speed as well. The result reveals the capability of the FCM clustered PNN in the computer-aided diagnosis of ECG abnormalities.
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Metadata
Title
Fuzzy Clustered Probabilistic and Multi Layered Feed Forward Neural Networks for Electrocardiogram Arrhythmia Classification
Authors
Hassan Hamsa Haseena
Abraham T. Mathew
Joseph K. Paul
Publication date
01-04-2011
Publisher
Springer US
Published in
Journal of Medical Systems / Issue 2/2011
Print ISSN: 0148-5598
Electronic ISSN: 1573-689X
DOI
https://doi.org/10.1007/s10916-009-9355-9

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