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

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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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Authors and Affiliations

  1. Department of Electrical and Electronics Engineering, M.E.S. College of Engineering, Kuttippuram, 679 573, Kerala, India

    Hassan Hamsa Haseena

  2. Department of Electrical Engineering, National Institute of Technology, Calicut, 673 601, Kerala, India

    Abraham T. Mathew & Joseph K. Paul

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  1. Hassan Hamsa Haseena
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  2. Abraham T. Mathew
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  3. Joseph K. Paul
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Correspondence to Hassan Hamsa Haseena.

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Haseena, H.H., Mathew, A.T. & Paul, J.K. Fuzzy Clustered Probabilistic and Multi Layered Feed Forward Neural Networks for Electrocardiogram Arrhythmia Classification. J Med Syst 35, 179–188 (2011). https://doi.org/10.1007/s10916-009-9355-9

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  • DOI: https://doi.org/10.1007/s10916-009-9355-9

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