Characterization of ventricular arrhythmias using a semantic mining algorithm

Abstract

Ventricular arrhythmia, especially ventricular fibrillation, is a type of arrhythmia that can cause sudden death. The aim of this paper is to characterize ventricular arrhythmias using semantic mining by extracting their significant characteristics (frequency, damping coefficient and input signal) from electrocardiogram (ECG) signals that represent the biological behavior of the cardiovascular system. Real data from an arrhythmia database are used after noise filtering and were statistically classified into two groups; normal sinus rhythm (N) and ventricular arrhythmia (V). The proposed method achieved high sensitivity and specificity (98.1% and 97.7%, respectively) and was capable of describing the differences between the N and V types in the ECG signal.