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Predicting Catheter Ablation Outcome in Persistent Atrial Fibrillation via Multivariate Analyses of ECG Fibrillatory Wave Amplitude

Topics: Atrial Fibrillation Ablation (Radiofrequency vs Cryoablation); Long Term ECG Monitoring; Risk Stratification Markers (Heart Rate Variability, Heart Rate Turbulence, Etc)

Authors: A. R. Hidalgo-Muñoz 1 ; V. Zarzoso 1 ; M. Meo 2 ; O. Meste 1 ; D. G. Latcu 3 ; I. Popescu 3 and N. Saoudi 3

Affiliations: 1 Université Nice Sophia Antipolis and CNRS, France ; 2 Brigham and Women's Hospital and Harvard Medical School, United States ; 3 Centre Hospitalier Princesse Grace, Monaco

Keyword(s): Atrial fibrillation, catheter ablation, ECG, f-wave, logistic regression, support vector machine

Related Ontology Subjects/Areas/Topics: Atrial Fibrillation Ablation (Radiofrequency vs Cryoablation) ; Cardiovascular Technologies ; Electrocardiology and Biological Signal Processing ; Electrophysiology: New Pacing and Ablation Technics ; Health Engineering and Technology Applications ; Long Term ECG Monitoring ; Risk Stratification Markers (Heart Rate Variability, Heart Rate Turbulence, Etc)

Abstract: Selecting the most suitable atrial fibrillation (AF) patients who can benefit from the catheter ablation (CA) therapy is a concern for cardiologists. Despite the improvement on CA techniques and the high rate of succesful procedures, objectively predicting its outcome is a challenging task. Some parameters computed from the ECG recordings can be used to classify the long-term AF manifestations that are more difficult to treat with CA. Usually, prediction models for CA outcome classification focus on only one lead (mainly V1). However, feature extraction methods can take advantage of the multiple ECG leads. This paper presents a combination of fibrillatory wave amplitude mean values measured in different ECG leads. We demonstrate that CA outcome prediction performance is enhanced if these variables are taken together. The improvement in terms of accuracy relative to single-lead prediction results reported in the literature is shown by applying two methodologies: logistic regression and support vector machines. Both algorithms point to I, V1, V2 and V5 as the most relevant ECG channels to predict CA outcome, reaching up to 82.3% accuracy. (More)

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Paper citation in several formats:
R. Hidalgo-Muñoz, A.; R. Hidalgo-Muñoz, A.; Zarzoso, V.; Meo, M.; Meste, O.; G. Latcu, D.; Popescu, I. and Saoudi, N. (2014). Predicting Catheter Ablation Outcome in Persistent Atrial Fibrillation via Multivariate Analyses of ECG Fibrillatory Wave Amplitude.In - CARDIOTECHNIX, ISBN , pages 0-0

@conference{cardiotechnix14,
author={A. R. Hidalgo{-}Muñoz. and A. R. Hidalgo{-}Muñoz. and V. Zarzoso. and M. Meo. and O. Meste. and D. G. Latcu. and I. Popescu. and N. Saoudi.},
title={Predicting Catheter Ablation Outcome in Persistent Atrial Fibrillation via Multivariate Analyses of ECG Fibrillatory Wave Amplitude},
booktitle={ - CARDIOTECHNIX,},
year={2014},
pages={},
publisher={SciTePress},
organization={INSTICC},
doi={},
isbn={},
}

TY - CONF

JO - - CARDIOTECHNIX,
TI - Predicting Catheter Ablation Outcome in Persistent Atrial Fibrillation via Multivariate Analyses of ECG Fibrillatory Wave Amplitude
SN -
AU - R. Hidalgo-Muñoz, A.
AU - R. Hidalgo-Muñoz, A.
AU - Zarzoso, V.
AU - Meo, M.
AU - Meste, O.
AU - G. Latcu, D.
AU - Popescu, I.
AU - Saoudi, N.
PY - 2014
SP - 0
EP - 0
DO -

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