Modified multiple Hurst index for evaluating measure of chaoticity in cardiac arrhythmias classification application

The humans’ heart is a chaotic system so use of fractal dimension to identify cardiac arrhythmias has been considered. Cardiac arrhythmias are prevalent diseases that is very important to be diagnosed. Hurst index which is calculated using rescaled range analysis method, is used as a criterion to evaluate chaotic systems and to quantify the fractal dimensions. Previous studies have shown that classical Hurst index is not appropriate for classification of cardiac arrhythmias because not only selection of algorithm parameters affect the value of determined Hurst index, but also it significantly varies as the heart rate changes. In this paper, modified multiple Hurst index has been proposed to classify the cardiac arrhythmias. The presented index is resistant against changes in heart rate and can be used to identify appropriate features to classify the cardiac arrhythmias. 80 signal from four types of ECG beats (normal, left bundle branch block, right bundle branch block and atrial premature beats) obtained from the MIT-BIH Arrhythmia dataset has been used to validate the algorithm. Results show that this method is able to detect normal rhythm and right bundle branch block (RBBB), left bundle branch block (LBBB) and atrial premature complex (APC) arrhythmias with accuracy of 100%, 96.25% and 88.75% using artificial neural network, k nearest neighbor and LDA classifiers respectively.