title: Automatic supporting system for regionalization of ventricular tachycardia exit site in implantable defibrillators.
abstract: Electrograms stored in Implantable Cardioverter Defibrillators (ICD-EGM) have been proven to convey useful information for roughly determining the anatomical location of the Left Ventricular Tachycardia exit site (LVTES). Our aim here was to evaluate the possibilities from a machine learning system intended to provide an estimation of the LVTES anatomical region with the use of ICD-EGM in the situation where 12-lead electrocardiogram of ventricular tachycardia are not available. Several machine learning techniques were specifically designed and benchmarked, both from classification (such as Neural Networks (NN), and Support Vector Machines (SVM)) and regression (Kernel Ridge Regression) problem statements. Classifiers were evaluated by using accuracy rates for LVTES identification in a controlled number of anatomical regions, and the regression approach quality was studied in terms of the spatial resolution. We analyzed the ICD-EGM of 23 patients (18±10 EGM per patient) during left ventricular pacing and simultaneous recording of the spatial coordinates of the pacing electrode with a navigation system. Several feature sets extracted from ICD-EGM (consisting of times and voltages) were shown to convey more discriminative information than the raw waveform. Among classifiers, the SVM performed slightly better than NN. In accordance with previous clinical works, the average spatial resolution for the LVTES was about 3 cm, as in our system, which allows it to support the faster determination of the LVTES in ablation procedures. The proposed approach also provides with a framework suitable for driving the design of improved performance future systems.
authors.affiliation: Department of Signal Theory and Communications, Telematics and Computing, Universidad Rey Juan Carlos, Fuenlabrada 28943, Spain.
authors.forename: Margarita
authors.initials: M
authors.lastname: Sanromán-Junquera
authors.affiliation: Department of Signal Theory and Communications, Telematics and Computing, Universidad Rey Juan Carlos, Fuenlabrada 28943, Spain.
authors.forename: Inmaculada
authors.initials: I
authors.lastname: Mora-Jiménez
authors.affiliation: Arrhythmia Unit, Hospital Madrid Montepríncipe, Boadilla del Monte 28660, Spain.
authors.forename: Jesús
authors.initials: J
authors.lastname: Almendral
authors.affiliation: Arrhythmia Unit, Hospital Universitario Virgen de la Arrixaca, Murcia 30120, Spain.
authors.forename: Arcadio
authors.initials: A
authors.lastname: García-Alberola
authors.affiliation: Department of Signal Theory and Communications, Telematics and Computing, Universidad Rey Juan Carlos, Fuenlabrada 28943, Spain; Universidad de las Fuerzas Armadas-ESPE, Sangolquí 171-5-231B, Ecuador.
authors.forename: José Luis
authors.initials: JL
authors.lastname: Rojo-Álvarez
mesh_terms: D000368:Aged; D017147:Defibrillators, Implantable; D004562:Electrocardiography; D005260:Female; D006801:Humans; D008297:Male; D008875:Middle Aged; D015203:Reproducibility of Results; D060388:Support Vector Machine; D017180:Tachycardia, Ventricular / Q000175:diagnosis* / Q000503:physiopathology*