Catheter ablation outcome prediction in persistent atrial fibrillation using weighted principal component analysis
Highlights
► Atrial signal spatial spatio-temporal orgarnization measures are exploited for catheter ablation outcome in persistent AF. ► These measures are determined by WPCA, for the first time applied to ECG processing for AF analysis. ► WPCA allows the exploitation of ECG multivariate properties and the enhancement of the most descriptive leads. ► Classification performance of our multilead descriptor outperforms that of classical single-lead predictors. ► It proves to be more robust to lead selection as well.
Introduction
Atrial fibrillation (AF) is a sustained cardiac arrhythmia characterized by rapid and disorganized atrial activations inducing a loss of atrial mechanical efficacy. Several theories have been suggested to explain AF electrophysiological mechanisms, so as to put forth a systematic procedural protocol for its treatment. AF activity has been first regarded as the result of interactions between multiple wandering atrial wavelets [1], [2]. On the other hand, it is commonly acknowledged that pulmonary veins (PVs) significantly contribute to AF maintenance and evolution, especially in paroxysmal forms of this disease [3]. In spite of major advances in its treatment, AF remains a significant cause of cardiovascular morbidity and mortality, especially those arising from stroke and heart failure.
Radiofrequency catheter ablation (CA) has become the first-line strategy [4] for the treatment of this disease. However, as the precise pathophysiology of AF dynamics has not been completely clarified yet, it is still questionable whether CA effectively suppresses abnormal rhythm sources, and how it affects heart electrical substrate. Different CA techniques have been developed, yet none of them is widely considered as effective for the treatment of persistent AF. Their performance is still far from satisfactory, and they are less effective than equivalent procedures for paroxysmal AF. Since this cardiac interventional procedure is profoundly influenced by operator's experience and patient's health conditions, results reported by clinical centers are quite disparate and not easily comparable [5], [6], [7]. It follows that its efficacy in terminating AF and avoiding its recurrence is not guaranteed for all patients. This situation explains the increasing tendency to attempt an a priori selection of patients who can undergo CA and experience durable sinus rhythm (SR) restoration. Several parameters extracted from the surface ECG have been proposed as potential predictors of CA outcome [8], [9]. For example, prolongation of atrial fibrillation cycle length (AFCL) can be associated with AF termination by CA [10]. In other studies [8], it has been argued that the higher the amplitude of the fibrillatory waves (f-waves) observed on the surface ECG, the more likely procedural success.
In parallel, another line of research aims at noninvasive measures of AF spatio-temporal complexity, with the underlying assumptions that treatment modalities should be chosen and therapy outcome could be predicted on the basis of these measures. In [11], a noninvasive measure of AF organization is assessed by the normalized mean square error (NMSE) values between the atrial activity (AA) signal and its rank-3 approximations determined by principal component analysis (PCA) in lead V1 [11]. This argument is supported by the hypothesis of a correlation between AF organization and the number and interactions of atrial wavefronts through the heart substrate. The choice of V1 is justified by the fact that it presents the maximum atrial-to-ventricular amplitude ratio among all ECG leads [12]. In [13], CA performance was shown to influence AF spatio-temporal organization, and its effect can be quantified by variations in NMSE values.
Nevertheless, such parameters are affected by several shortcomings. In the first place, some classical ECG-based descriptors are manually computed [8], [10], so they are subject to operators’ subjectivity and thus prone to errors. Furthermore, as most of them are measured in only one ECG lead, they do not account for information that may be provided by other electrodes. Indeed, ECG analysis is not always straightforward, and visual inspection does not capture AF features underlying the whole ensemble of leads; hence, the limitations of classical single-lead techniques, which do not fully exploit multilead ECG spatial diversity. However, AF spatio-temporal complexity as defined in [11] has not been shown to correlate with CA outcome.
Our investigation focuses on the potential application of the spatio-temporal organization of AA measured on the standard ECG by the NMSE index as a tool to discriminate between successful and failing CA procedures before applying the therapy. Contributions provided from the eight independent ECG leads are expressed in terms on NMSE between successive segments of the actual AA signal and their rank-1 approximations computed by weighted principal component analysis (WPCA), and they are finally combined in a single parameter capable of predicting long-term CA outcome. Thanks to this decomposition, the spatial variability of the standard ECG is taken into account, and the most significant ECG leads are also automatically enhanced by assigning different weights to data based on their estimated relevance.
Section snippets
Characteristics and acquisition modalities of the persistent-AF database
Twenty patients (19 males, 60 ± 11 years) with a median persistent AF episode duration of 4.5 months (2–84) were enrolled in the present study. They all underwent CA at the Cardiology Department of Princess Grace Hospital in Monaco, performed with the aid of Prucka Cardiolab and Biosense CARTO electrophysiology measurement systems. They all gave their informed consent. Surface 12-lead ECG recordings were acquired at the beginning of the procedure, at a sampling rate of 1 kHz. An example of the
Results
Our 8-lead descriptor is compared with its 12-lead counterpart . Moreover, the final weighted mean of NMSE values has also been computed for each lead subset after performing a rank-1 approximation by classical PCA, thus obtaining and , respectively. A comparison between multilead descriptors and conventional single-lead methods is drawn as well. Accordingly, AA amplitude D(V1) is computed on lead V1 according to the algorithm proposed in [37], [38]. Moreover,
Discussion
This work investigates noninvasive measures of AA spatio-temporal variability and their link to CA outcome prediction in persistent AF. The main results can be summarized as follows. Firstly, spatial variability of the standard ECG proves to be a useful tool to describe AF content and offer a wider perspective about the evolution of the disease during CA, thus helping its outcome prediction. In the second place, an index conventionally employed as a classifier of AF organization type is herein
Conclusions
This work has examined the role of quantitative indices computed on the surface standard ECG in predicting CA outcome. These parameters are derived from the NMSE of reduced-rank PCA approximations to the AA signal, recently shown to quantify AF spatio-temporal organization. Even though we have not proved their ability to assess AF organization, our investigation has demonstrated that contributions from several ECG leads can be adequately combined so as to accomplish preprocedural long-term CA
Acknowledgements
This work is partly supported by the French National Research Agency under contract ANR-2010-JCJC-0303-01 “PERSIST”. Marianna Meo is funded by a doctoral grant from the French Ministry of Higher Education and Research. Her activity is also funded by a one-year grant awarded in 2012 by the DreamIT Foundation in partnership with the University of Nice Sophia Antipolis.
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Cited by (16)
Short-term reproducibility of parameters characterizing atrial fibrillatory waves
2020, Computers in Biology and MedicineCitation Excerpt :In recent years, a wide range of parameters have been proposed for characterizing atrial fibrillatory waves (f-waves) in the surface ECG [1–8]. The parameters have been linked to various clinical aspects, including left atrial size [9], spontaneous termination of paroxysmal atrial fibrillation (AF) [10,11], catheter ablation outcome [12–16], and the effect of rate-control drugs [17–19]. The overall aim of the proposed f-wave parameters is to help improve patient-tailored diagnosis and therapy of AF.
Spectral and spatiotemporal variability ECG parameters linked to catheter ablation outcome in persistent atrial fibrillation
2017, Computers in Biology and MedicineCitation Excerpt :However, that work does not analyze the ability of spectral turbulence to predict CA outcome and takes into account only one ECG lead. Nevertheless, we have considered the STV measurement [20] the most suitable to the aim of this study, since it had been previously incorporated in algorithms dealing with the analysis of CA outcome [38]. The NMSE proposed in that work is an intuitive measurement of STV that compares directly different ECG segments over time.
Recurring patterns of atrial fibrillation in surface ECG predict restoration of sinus rhythm by catheter ablation
2014, Computers in Biology and MedicineCitation Excerpt :Our results are in contrast with another study on surface ECG [25], in which a weighted PCA-based method predicted postoperative CA outcome in a sample of 20 persistent-AF patients over a variable (on a patient basis) follow-up period between 4 and 19 months. Although the discordance could at least in part be explained by the different sample size and variable follow-up period in [25], the significant predictivity obtained in the above study suggests that more advanced multi-lead approaches could improve the predictivity of CA clinical outcome. However, these results support the validity of recurrence plot analysis applied to TQ intervals only, avoiding the need for the laborious QRS-T cancellation.