Detection of cardiac arrhythmias using a damped exponential modeling algorithm

We describe a new approach for the discrimination among ventricular fibrillation (VF), ventricular tachycardia (VT) and superventricular tachycardia (SVT) based on a damped exponential (DE) modeling algorithm. Two features, dubbed energy fractional factor (EFF) and predominant frequency (PF), were derived from the DE model. Classification task is achieved by performing a two-stage process using the EFF and PF indicators. Tests conducted using 91 episodes drawn from the MIT-BIH database produced total predictive accuracy of (SVT,VF,VT)=(95%,96%,98%)