Type-2 Fuzzy System for ECG Arrhythmic Classification

This paper aims at assessing the feasibility of using a type-2 fuzzy system for ECG arrhythmic beat classification. Three types of ECG signals, namely the normal sinus rhythm (NSR), ventricular fibrillation (VF) and ventricular tachycardia (VT), are considered. The inputs to the fuzzy classifier are the average period and the pulse width, two features that are commonly used for computer-assisted arrhythmia recognition and are readily extracted from pre-processed ECG waveforms. Using a combination of the fuzzy C-means clustering algorithm and the amount of dispersion in each cluster, a method for designing the antecedent type-2 MFs of the classifier from a training data set is formulated. Tests using data from the MIT-BIH Arrhythmia Database show that the proposed type-2 fuzzy classifier yields an accuracy of 90.91 % for VT events and 84 % for VF events and 100 % for NSR events.