A neuro-fuzzy identification of ECG beats.
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.
Chikh
Mohammed Amine
MA
Biomedical Engineering Laboratory, Tlemcen University, Tlemcen, Algeria. mea_chikh@mail.univ-tlemcen.dz
Ammar
Mohammed
M
Marouf
Radja
R
D004562:Electrocardiography; D017143:Fuzzy Logic; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D016571:Neural Networks, Computer; D012680:Sensitivity and Specificity; D018879:Ventricular Premature Complexes / Q000175:diagnosis*