Automated detection of fibrillations and flutters based on fused feature set and ANFIS classifier

Abstract

One of the major causes of human death worldwide is cardiac arrhythmia. Atrial Fibrillation, Atrial Flutter and Ventricular Fibrillation are considered the most prevalent among the various arrhythmia types. Hence, computer-aided detection (CAD) system has been developed to help the clinicians to interpret the electrocardiogram signal reliably within short period of time. The proposed ranking based scheme and weight factor analysis for the construction of fused feature set improve the classification accuracy compared to the latest available methods in the literature. The diagnostic ability of the CAD system has been shown in seven performance measure parameter analysis. The best classification result has been obtained using Adaptive Neuro-Fuzzy Interface System (ANFIS) classifier with the fused feature set. The proposed system of using ANFIS classifier has achieved average classification accuracy of 99.88%, precision of 99.25% and recall of 99.98%. The proposed system using ANFIS algorithm has high recognition rate compared with that of Support Vector Machine and Random Forest classifiers. Our proposed model is significantly efficient to discriminate the normal electrocardiogram signal and three cardiac arrhythmia types.