A Neuro-Fuzzy Identification of ECG Beats

Authors:
Mohammed Amine Chikh;Mohammed Ammar;Radja Marouf
Affiliations:
Biomedical Engineering Laboratory, Tlemcen University, Tlemcen, Algeria;Biomedical Engineering Laboratory, Tlemcen University, Tlemcen, Algeria;Biomedical Engineering Laboratory, Tlemcen University, Tlemcen, Algeria
Venue:
Journal of Medical Systems
Year:
2012

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Abstract

This paper presents a fuzzy rule based classifier and its application to discriminate premature ventricular contraction (PVC) beats from normals. An Adaptive Neuro-Fuzzy Inference System (ANFIS) is applied to discover the fuzzy rules in order to determine the correct class of a given input beat. The main goal of our approach is to create an interpretable classifier that also provides an acceptable accuracy. The performance of the classifier is tested on MIT-BIH (Massachusetts Institute of Technology-Beth Israel Hospital) arrhythmia database. On the test set, we achieved an overall sensitivity and specificity of 97.92% and of 94.52% respectively. Experimental results show that the proposed approach is simple and effective in improving the interpretability of the fuzzy classifier while preserving the model performances at a satisfactory level.