A new approach for ICD rhythm classification based on support vector machines.
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.
Kamousi
B
B
School of Medicine, Stanford University, Stanford, CA, USA.
Tewfik
A
A
Lin
B
B
Al-Ahmad
A
A
Hsia
H
H
Zei
P
P
Wang
P
P
D000465:Algorithms; D001145:Arrhythmias, Cardiac; D002309:Cardiology / Q000379:methods; D003198:Computer Simulation; D017147:Defibrillators, Implantable; D004562:Electrocardiography / Q000379:methods*; D006339:Heart Rate; D006801:Humans; D010363:Pattern Recognition, Automated / Q000379:methods; D015203:Reproducibility of Results; D012815:Signal Processing, Computer-Assisted; D012984:Software; D017180:Tachycardia, Ventricular / Q000175:diagnosis*; D013813:Therapy, Computer-Assisted / Q000379:methods