Prediction of atrial fibrillation recurrence before catheter ablation using an adaptive nonlinear and non-stationary surface ECG analysis

Xingran Cui, Hung Chi Chang, Lian Yu Lin, Chih Chieh Yu, Wan Hsin Hsieh, Weihui Li, Chung Kang Peng, Jiunn-Lee Lin, Men Tzung Lo

Research output: Contribution to journalArticle

Abstract

The recurrence rate of atrial fibrillation (AF) following catheter ablation is high. Successful non-recurrence prediction before ablation is financially and physically beneficial. Atrial fibrillation cycle length (AFCL) has been used for such predictions. The study explored a nonlinear and non-stationary signal analysis technique, ensemble empirical model decomposition (EEMD), to capture intrinsic AFCL pattern. Twenty-eight AF patients underwent regular catheter ablation protocols and follow-up treatments. Surface ECG were recorded before ablation procedures, and preprocessed with QRS-T cancellation method to extract intrinsic AF signals, which were then analyzed with EEMD to calculate AFCL adaptively. Conventional Fourier-based dominant frequency (DF) analysis was conducted for comparison. During 12 months follow-up, 17 patients maintained sinus rhythm while 11 patients had AF recurrence. Patients who maintained sinus rhythm had longer AFCL (152.2 (145.0, 157.6) ms) than those with AF recurrence (138.8 (135.0, 145.2) ms). Patients with AFCL >152.1 ms had no recurrence (Kaplan–Meier curve analysis, P=0.006, hazard ratio: 5.68). With AFCL as a predictor of AF non-recurrence at 12 months, the area under ROC curve is 0.80, while with DF analysis, the area is 0.66. EEMD-based intrinsic AFCL measured from surface ECG may serve as an effective non-invasive catheter ablation screening tool for AF non-recurrence.

LanguageEnglish
Pages9-19
Number of pages11
JournalPhysica A: Statistical Mechanics and its Applications
Volume514
DOIs
Publication statusPublished - Jan 15 2019
Externally publishedYes

Fingerprint

fibrillation
Atrial Fibrillation
Ablation
Recurrence
ablation
Cycle Length
Prediction
predictions
cycles
Empirical Model
sinuses
rhythm
Frequency Analysis
Ensemble
Electrocardiogram
decomposition
Decompose
Kaplan-Meier
Non-stationary Signal
Signal Analysis

Keywords

  • Atrial fibrillation cycle length
  • Ensemble empirical mode decomposition
  • Instantaneous phase
  • Nonlinear and nonstationary
  • Surface ECG

ASJC Scopus subject areas

  • Statistics and Probability
  • Condensed Matter Physics

Cite this

Prediction of atrial fibrillation recurrence before catheter ablation using an adaptive nonlinear and non-stationary surface ECG analysis. / Cui, Xingran; Chang, Hung Chi; Lin, Lian Yu; Yu, Chih Chieh; Hsieh, Wan Hsin; Li, Weihui; Peng, Chung Kang; Lin, Jiunn-Lee; Lo, Men Tzung.

In: Physica A: Statistical Mechanics and its Applications, Vol. 514, 15.01.2019, p. 9-19.

Research output: Contribution to journalArticle

Cui, Xingran ; Chang, Hung Chi ; Lin, Lian Yu ; Yu, Chih Chieh ; Hsieh, Wan Hsin ; Li, Weihui ; Peng, Chung Kang ; Lin, Jiunn-Lee ; Lo, Men Tzung. / Prediction of atrial fibrillation recurrence before catheter ablation using an adaptive nonlinear and non-stationary surface ECG analysis. In: Physica A: Statistical Mechanics and its Applications. 2019 ; Vol. 514. pp. 9-19.
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