ABSTRACT
Inappropriate shocks due to misclassification of supraventricular and ventricular arrhythmias remain a major problem in the care of patients with Implantable Cardioverter Defibrillators (ICDs). The purpose of this study was to investigate the ability of a new covariance-based support vector machine classifier, to distinguish ventricular tachycardia from other rhythms such as supraventricular tachycardia. The proposed algorithm is applicable on both single and dual chamber ICDs and has a low computational demand. The results demonstrate that suggested algorithm has considerable promise and merits further investigation.
Subject(s)
Defibrillators, Implantable , Electrocardiography/methods , Tachycardia, Ventricular/diagnosis , Algorithms , Arrhythmias, Cardiac , Cardiology/methods , Computer Simulation , Heart Rate , Humans , Pattern Recognition, Automated/methods , Reproducibility of Results , Signal Processing, Computer-Assisted , Software , Therapy, Computer-Assisted/methodsABSTRACT
Inappropriate shocks due to misclassification of supraventricular and ventricular arrhythmias remain a major problem in the care of patients with Implantable Cardioverter defibrillators (ICDs). In this study we have investigated the ability of a new covariance-based algorithm, to distinguish Ventricular Tachycardia from other rhythms such as Supraventricular Tachycardia. The proposed algorithm has a low computational demand and with a small adjustment is applicable on both single-chamber and dual-chamber ICDs. The results are promising and suggest that the new covariance-based algorithm may be an effective method for ICD rhythm classification and may decrease inappropriate shocks.
