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A neuro-fuzzy identification of ECG beats
Mohammed Amine Chikh1, Mohammed Ammar, Radja Marouf
1Biomedical Engineering Laboratory, Tlemcen University, Tlemcen, Algeria. mea_chikh@mail.univ-tlemcen.dz
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.
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