Finotti, E.; Ciaccio, EJ.; Garan, H.; Bertomeu-González, V.; Alcaraz, R.; Rieta, JJ. (2020). A Straightforward Methodology to Distinguish Complex Fractionated Atrial Electrograms of Paroxysmal from Persistent Atrial Fibrillation. IEEE. 1-4. https://doi.org/10.1109/EHB50910.2020.9280231
Por favor, use este identificador para citar o enlazar este ítem: http://hdl.handle.net/10251/179849
Title:
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A Straightforward Methodology to Distinguish Complex Fractionated Atrial Electrograms of Paroxysmal from Persistent Atrial Fibrillation
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Author:
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Finotti, Emanuela
Ciaccio, Edward J.
Garan, Hasan
Bertomeu-González, Vicente
Alcaraz, Raúl
Rieta, J J
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UPV Unit:
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Universitat Politècnica de València. Departamento de Ingeniería Electrónica - Departament d'Enginyeria Electrònica
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Issued date:
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Abstract:
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[EN] Many indices aimed at discriminating between paroxysmal and persistent atrial fibrillation (ParAF vs. PerAF) have been previously studied and assessed via statistical tests in order to suggest optimized approaches to ...[+]
[EN] Many indices aimed at discriminating between paroxysmal and persistent atrial fibrillation (ParAF vs. PerAF) have been previously studied and assessed via statistical tests in order to suggest optimized approaches to catheter ablation (CA) of AF. However, clinicians demand the use of simple classification methods of straightforward comprehension. The present work exploits AF cycle length (AFCL), dominant frequency (DF), sample entropy (SE) and determinism (DET) of recurrent quantification analysis, applied to AF recordings of complex fractionated atrial electrograms (CFAEs), aimed at creating straightforward models to discriminate between ParAF and PerAF. AFCL and DF were calculated on the full AF recordings, whereas SE and DET were computed on segments of 1, 2 and 4s. First, correlation matrix filters removed redundant information and Random Forests made a ranking of variables by relevance. Next, coarse tree classificators were created, combining optimally high-ranked indexes which were tested with leave-one-out cross-validation. After analyzing all the possible combinations of highly ranked features, the best classification performance provided an Accuracy (Acc) of 88.2% to discriminate ParAF from PerAF, while DET provided the highest single Acc of 82.4%. As conclusion, the careful selection of limited sets of indices feeding straightforward classificators are able to discriminate accurately between CFAEs of ParAF and PerAF.
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Subjects:
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Atrial fibrillation
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Recurrent quantification analysis
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Sample entropy
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Electrogram fractionation
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CFAE
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Copyrigths:
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Cerrado |
ISBN:
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978-1-7281-8803-4
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Source:
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2020 E-Health and Bioengineering Conference (EHB).
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DOI:
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10.1109/EHB50910.2020.9280231
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Publisher:
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IEEE
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Publisher version:
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https://doi.org/10.1109/EHB50910.2020.9280231
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Conference name:
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8th International Conference on e-Health and Bioengineering (EHB 2020)
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Conference place:
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Online
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Conference date:
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Octubre 29-30,2020
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Project ID:
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info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2013-2016/DPI2017-83952-C3-1-R/ES/ESTUDIO MULTICENTRICO PARA LA EVALUACION DEL SUSTRATO ARRITMOGENICO EN PACIENTES CON FIBRILACION AURICULAR. APLICACION A LA ABLACION POR CATETER/
info:eu-repo/grantAgreement/JCCM//SBPLY%2F17%2F180501%2F000411//Caracterización del sustrato auricular mediante análisis de señal como herramienta de asistencia procedimental en ablación por catéter de fibrilación auricular/
info:eu-repo/grantAgreement///AICO%2F2019%2F036//METODOS DE DIAGNOSTICO Y TERAPIA PERSONALIZADA EN ABLACION POR CATETER DE ARRITMIAS CARDIACAS/
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Description:
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© 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
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Thanks:
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Research supported by grants DPI2017-83952-C3 from
MINECO/AEI/FEDER UE, SBPLY/17/180501/000411 from
JCCLM and AICO/2019/036 from GVA.
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Type:
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Comunicación en congreso
Capítulo de libro
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