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Automatic supporting system for regionalization of ventricular tachycardia exit site in implantable defibrillators.

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...

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Autor Principal: Rojo ?lvarez, Jos? Luis
Formato: Artículos
Lenguaje:eng
Publicado: 2016
Materias:
AUTOMATIC SUPPORTING SYSTEM
REGIONALIZATION
VENTRICULAR TACHYCARDIA
IMPLANTABLE DEFIBRILLATORS
Acceso en línea:http://repositorio.educacionsuperior.gob.ec/handle/28000/3035
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spelling oai:localhost:28000-30352017-04-05T21:46:31Z Automatic supporting system for regionalization of ventricular tachycardia exit site in implantable defibrillators. Rojo ?lvarez, Jos? Luis AUTOMATIC SUPPORTING SYSTEM REGIONALIZATION VENTRICULAR TACHYCARDIA IMPLANTABLE DEFIBRILLATORS 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. Universidad De Las Fuerzas Armadas https://www.ncbi.nlm.nih.gov/pubmed/25910170 2016-11-09T16:57:53Z 2016-11-09T16:57:53Z 2015 article Sanrom?n Junquera, M. et al..(2015). Automatic supporting system for regionalization of ventricular tachycardia exit site in implantable defibrillators. Plos One v. 10(4). 1932-6203 http://repositorio.educacionsuperior.gob.ec/handle/28000/3035 eng openAccess
institution SENESCYT
collection Repositorio SENESCYT
biblioteca Biblioteca Senescyt
language eng
format Artículos
topic AUTOMATIC SUPPORTING SYSTEM
REGIONALIZATION
VENTRICULAR TACHYCARDIA
IMPLANTABLE DEFIBRILLATORS
spellingShingle AUTOMATIC SUPPORTING SYSTEM
REGIONALIZATION
VENTRICULAR TACHYCARDIA
IMPLANTABLE DEFIBRILLATORS
Rojo ?lvarez, Jos? Luis
Automatic supporting system for regionalization of ventricular tachycardia exit site in implantable defibrillators.
description 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.
author Rojo ?lvarez, Jos? Luis
author_facet Rojo ?lvarez, Jos? Luis
author_sort Rojo ?lvarez, Jos? Luis
title Automatic supporting system for regionalization of ventricular tachycardia exit site in implantable defibrillators.
title_short Automatic supporting system for regionalization of ventricular tachycardia exit site in implantable defibrillators.
title_full Automatic supporting system for regionalization of ventricular tachycardia exit site in implantable defibrillators.
title_fullStr Automatic supporting system for regionalization of ventricular tachycardia exit site in implantable defibrillators.
title_full_unstemmed Automatic supporting system for regionalization of ventricular tachycardia exit site in implantable defibrillators.
title_sort automatic supporting system for regionalization of ventricular tachycardia exit site in implantable defibrillators.
publishDate 2016
url http://repositorio.educacionsuperior.gob.ec/handle/28000/3035
_version_ 1634995102720983040
score 11,871979

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