Measures of spatiotemporal organization differentiate persistent from long-standing atrial fibrillation

Uldry, Laurent; Zaen, Jérôme Van; Prudat, Yann; Kappenberger, Lukas; Vesin, Jean‐Marc

doi:10.1093/europace/eur436

Cited by 32 publications

(34 citation statements)

References 19 publications

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Section: Ecg Analysismentioning

confidence: 99%

“…Frequency domain parameters included dominant frequency (DF), 14 organization index (OI), 15 and spectral entropy (SE). 15 Time domain parameters consisted of the mean fibrillation-wave amplitude (FWA) 16 and sample entropy (SampEn). 17 For the frequency domain parameters, a frequency power spectrum was estimated using Welch power spectral density estimate (3 segments; 4096 points; 50% overlap).…”

Section: Ecg Analysismentioning

confidence: 99%

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Atrial Fibrillation Complexity Parameters Derived From Surface ECGs Predict Procedural Outcome and Long-Term Follow-Up of Stepwise Catheter Ablation for Atrial Fibrillation

Lankveld

Zeemering

Scherr

et al. 2016

Circ: Arrhythmia and Electrophysiology

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Background-The success rate of catheter ablation for persistent atrial fibrillation (AF) is still far from satisfactory.Identification of patients who will benefit from ablation is highly desirable. We investigated the predictive value of noninvasive AF complexity parameters derived from standard 12-lead ECGs for AF termination and long-term success of catheter ablation and compared them with clinical predictors. Methods and Results-The study included a training (93 patients) and a validation set (81 patients) of patients with persistent AF undergoing stepwise radiofrequency ablation. In the training set AF terminated in 81% during catheter ablation, 77% were in sinus rhythm after 6 years and multiple ablations. ECG-derived complexity parameters were determined from a baseline 10-s 12-lead ECG. Prediction of AF termination was similar using only ECG (cross-validated mean area under the curve [AUC], 0.76±0.15) or only clinical parameters (mean AUC, 0.75±0.16). The combination improved prediction to a mean AUC of 0.79±0.13. Using a combined model of ECG and clinical parameters, sinus rhythm at long-term follow-up could be predicted with a mean AUC of 0.71±0.12. In the validation set AF terminated in 57%, 61% were in sinus rhythm after 4.6 years. The combined models predicted termination with an AUC of 0.70 and sinus rhythm at longterm follow-up with an AUC of 0.61. Overall, fibrillation-wave amplitude provided the best rhythm prediction. Conclusions-The predictive performance of ECG-derived AF complexity parameters for AF termination and long-term success of catheter ablation in patients with persistent AF is at least as good as known clinical predictive parameters, with fibrillation-wave amplitude as the best predictor. (Circ Arrhythm Electrophysiol. 2016;9:e003354.

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Section: Ecg Analysismentioning

confidence: 99%

Section: Ecg Analysismentioning

confidence: 99%

Atrial Fibrillation Complexity Parameters Derived From Surface ECGs Predict Procedural Outcome and Long-Term Follow-Up of Stepwise Catheter Ablation for Atrial Fibrillation

Lankveld

Zeemering

Scherr

et al. 2016

Circ: Arrhythmia and Electrophysiology

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“…Performances show promising results in clinical practice, especially when taking into account that the population under study is heterogeneous in terms of related heart diseases, antiarrhythmic therapies and baseline characteristics. In Table 6, classification results for our heterogeneous dataset are compared with [29] and also the methods of several references that have been proposed for differentiating paroxysmal and persistent AF episodes. These references propose using Fourier analysis and non-linear parameters of atrial activity as features.…”

Section: Discussionmentioning

confidence: 99%

“…In fact, to our knowledge, this is the first study focused on persistent and long-standing persistent AF differentiation (at least the first one that includes patients with multiple pathologies treated with rate-control strategies [29]). Thus, the current literature presents many articles that classify paroxysmal and persistent atrial fibrillation episodes, but there is a lack of studies that try to distinguish persistent patients from those whose atrial fibrillation episodes are more likely to progress toward a permanent state (as longstanding patients have remained fibrillating for more than 1 year).…”

Section: Discussionmentioning

confidence: 99%

“…Moreover, to our knowledge, apart from the work presented in [29] (which recorded and evaluated ECG signals once antiarrhythmic treatments were restricted to patients and also excluded subjects with multiple pathologies), there are no previous references in the current literature that have analyzed persistent and long-standing persistent AF discrimination.…”

Section: Introductionmentioning

confidence: 99%

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Classification of persistent and long-standing persistent atrial fibrillation by means of surface electrocardiograms

Ortigosa

Fernández

Galbis

et al. 2015

Biomedical Engineering / Biomedizinische Technik

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Abstract:Atrial fibrillation, which is the most common cardiac arrhythmia, is typically classified into four clinical subtypes: paroxysmal, persistent, long-standing persistent and permanent. The ability to distinguish between them is of crucial significance in choosing the most suitable therapy for each patient. Nevertheless, classification is currently established once the natural history of the arrhythmia has been disclosed as it is not possible to make an early differentiation. This paper presents a novel method to discriminate persistent and long-standing atrial fibrillation patients by means of a time-frequency analysis of the surface electrocardiogram. Classification results provide approximately 75% accuracy when evaluating ECGs of consecutive unselected patients from a tertiary center and higher than 80% when patients are not under antiarrhythmic treatment or do not have structural heart disease (76% sensitivity and 88% specificity). Moreover, to our knowledge, this is the first study that discriminates between persistent and long-standing persistent subtypes in a heterogeneous population sample and without discontinuing antiarrhythmic therapy to patients. Thus, it can help clinicians to address the most suitable therapeutic approach for each patient.

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Characterization of f Waves

Sörnmo

Alcaraz

Laguna

et al. 2018

Series in BioEngineering

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Measures of spatiotemporal organization differentiate persistent from long-standing atrial fibrillation

Cited by 32 publications

References 19 publications

Atrial Fibrillation Complexity Parameters Derived From Surface ECGs Predict Procedural Outcome and Long-Term Follow-Up of Stepwise Catheter Ablation for Atrial Fibrillation

Atrial Fibrillation Complexity Parameters Derived From Surface ECGs Predict Procedural Outcome and Long-Term Follow-Up of Stepwise Catheter Ablation for Atrial Fibrillation

Classification of persistent and long-standing persistent atrial fibrillation by means of surface electrocardiograms

Characterization of f Waves

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