Characterization of Ventricular Arrhythmias in Electrocardiogram Signal Using Semantic Mining Algorithm

Ventricular arrhythmias, especially ventricular fibrillation, is a type of arrhythmias that can cause sudden death. The paper applies semantic mining approach to electrocardiograph (ECG) signals in order to extract its significant characteristics (frequency, damping coefficient and input signal) to be used for classification purpose. Real data from an arrhythmia database are used after noise filtration. After features extraction they are statistically classified into three groups, i.e. normal (N), normal patients (PN) and patients with ventricular arrhythmia (V). We found that the V, PN, and N types of ECG signals can be identified by the extracted parameters. It is estimated that the parameters in semantic algorithm can be use to predict the onset of ventricular arrhythmias.