J Med Syst - A neuro-fuzzy identification of ECG beats.

Tópicos

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Resumo

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.

Resumo Limpo

paper present fuzzi rule base classifi applic discrimin prematur ventricular contract pvc beat normal adapt neurofuzzi infer system anfi appli discov fuzzi rule order determin correct class given input beat main goal approach creat interpret classifi also provid accept accuraci perform classifi test mitbih massachusett institut technologybeth israel hospit arrhythmia databas test set achiev overal sensit specif respect experiment result show propos approach simpl effect improv interpret fuzzi classifi preserv model perform satisfactori level

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