Detecting ventricular arrhythmias by NEWFM

Abstract

The ventricular arrhythmias including ventricular tachycardia (VT) and ventricular fibrillation (VF) are life-threatening heart diseases. This paper presents an approach to detect normal sinus rhythm (NSR) and VF/VT using the neural network with weighted fuzzy membership functions (NEWFM). NEWFM classifies NSR and VF/VT beats by the trained bounded sum of weighted fuzzy membership functions (BSWFMs) using one input features from the Creighton University Ventricular Tachyarrhythmia Data Base (CUDB). In this paper, six input features are obtained from two steps. In the first step, 8s original ECG signal are transformed by Haar wavelet function, and then 256 coefficients of d3 at levels 3 are obtained. In the second step, six input features are obtained by phase space reconstruction (PSR) algorithm using 256 coefficients of d3 at levels 3. The one generalized feature is extracted by the non-overlap area distribution measurement method. The one generalized feature is used for the VF/VT data sets with reliable accuracy and specificity rates of 90.1% and 92.2%, respectively.