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Annals of Biomedical Engineering

, Volume 44, Issue 11, pp 3307–3318 | Cite as

Electrocardiographic Spectral Features for Long-Term Outcome Prognosis of Atrial Fibrillation Catheter Ablation

Article

Abstract

Atrial fibrillation (AF) is the most common arrhythmia in routine clinical practice. Despite many years of research, its mechanisms still are not well understood, thus reducing the effectiveness of AF treatments. Nowadays, pulmonary vein isolation by catheter ablation is the treatment of choice for AF resistant either to pharmacological or electrical cardioversion. However, given that long-term recurrences are common, an appropriate patient selection before the procedure is of paramount relevance in the improvement of AF catheter ablation outcome. The present work studies how several spectral features of the atrial activity (AA) from a single lead of the surface electrocardiogram (ECG) can become potential pre-ablation predictors of long-term (>2 months) sinus rhythm maintenance. Among all the analyzed spectral features, results indicated that the most significant single predictor of paroxysmal AF ablation treatment outcome was related to the amplitude of the first harmonic of the dominant frequency, providing sensitivity (Se), specificity (Sp) and accuracy (Ac) values of 90%, 42.86 and 77.78%, respectively. On the other hand, the AA harmonic structure was the most significant single predictor for persistent AF, with Se, Sp and Ac values of 100%, 54.55 and 77.27%, respectively. A logistic regression analysis, mainly based on spectral amplitudes as well as on the harmonic structure of the AA, provided a higher predictive ability both for paroxysmal AF (Se = 100%, Sp = 57.14% and Ac = 88.89%) and persistent AF (Se = 90.91%, Sp = 72.73 and Ac = 81.82%). In conclusion, the study of key AA spectral features from the surface ECG can provide a significant preoperative prognosis of AF catheter ablation outcome at long-term follow-up.

Keywords

Atrial fibrillation Catheter ablation Electrocardiogram Spectral analysis 

Notes

Acknowledgments

Research funded by Grants TEC2014-52250-R from the Spanish Ministry of Economy and Competitiveness and PPII-2014-026-P from Junta de Comunidades de Castilla-La Mancha.

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Copyright information

© Biomedical Engineering Society 2016

Authors and Affiliations

  • Raúl Alcaraz
    • 1
    • 2
  • Fernando Hornero
    • 3
  • José J. Rieta
    • 4
  1. 1.Research Group in Electronic, Biomedical and Telecommunications EngineeringUniversity of Castilla-La ManchaCuencaSpain
  2. 2.Escuela PolitécnicaCuencaSpain
  3. 3.Cardiac Surgery DepartmentGeneral University Hospital Consortium of ValenciaValenciaSpain
  4. 4.Biomedical Synergy, Electronic Engineering DepartmentUniversidad Politécnica de Valencia ValenciaSpain

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