Tachycardia Discrimination in Implantable Cardioverter Defibrillators Using Support Vector Machines and Bootstrap Resampling

[1]  Robert Tibshirani,et al.  An Introduction to the Bootstrap , 1994 .

[2]  Vladimir N. Vapnik,et al.  The Nature of Statistical Learning Theory , 2000, Statistics for Engineering and Information Science.

[3]  Bernhard Schölkopf,et al.  Support vector learning , 1997 .

[4]  J.L. Rojo-Alvarez,et al.  Discriminating between supraventricular and ventricular tachycardias from EGM onset analysis , 2002, IEEE Engineering in Medicine and Biology Magazine.

[5]  A. Artes-Rodriguez,et al.  Support vector black-box interpretation in ventricular arrhythmia discrimination , 2002, IEEE Engineering in Medicine and Biology Magazine.

[6]  Joseph D. Bronzino,et al.  The Biomedical Engineering Handbook , 1995 .

[7]  Bramahn . Singh,et al.  Controlling cardiac arrhythmias: an overview with a historical perspective. , 1997, The American journal of cardiology.

[8]  J. M. Jenkins,et al.  Detection algorithms in implantable cardioverter defibrillators , 1996, Proc. IEEE.

[9]  S. Hohnloser,et al.  Intracardiac QRS Electrogram Width—An Arrhythmia Detection Feature for Implantable Cardioverter Defibrillators: Exercise Induced Variation as a Base for Device Programming , 1998, Pacing and clinical electrophysiology : PACE.

[10]  I. Singer,et al.  Implantable Cardioverter Defibrillator , 1994 .

[11]  J. Rojo-álvarez,et al.  A new algorithm for rhythm discrimination in cardioverter defibrillators based on the initial voltage changes of the ventricular electrogram. , 2003, Europace : European pacing, arrhythmias, and cardiac electrophysiology : journal of the working groups on cardiac pacing, arrhythmias, and cardiac cellular electrophysiology of the European Society of Cardiology.