Detecting ventricular fibrillation using efficient techniques for computing a normalized autocorrelation.
With the introduction of the multifunction implantable pacemaker/cardioverter/defibrillator, it is increasingly important to detect and identify arrhythmias automatically. Detection of ventricular fibrillation by analysis of the autocorrelation function is widely used on surface lead ECG analysis, but due to the computational demand is not practical for use in an implantable defibrillator. In this paper, results using three computationally efficient algorithms for estimating the normalized autocorrelation are compared with the true normalized autocorrelation for discriminating polymorphic ventricular tachycardia/ventricular fibrillation (PMVT/VF) from monomorphic ventricular tachycardia (MVT) using signals available to an implantable defibrillator.
Department of Electrical Engineering, University of Nebraska, Lincoln 68588-0511.
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D000465:Algorithms; D017147:Defibrillators, Implantable; D004562:Electrocardiography / Q000379:methods* / Q000706:statistics & numerical data; D006801:Humans; D016011:Normal Distribution; D014693:Ventricular Fibrillation / Q000175:diagnosis* / Q000628:therapy