ECG periodic components as a promising tool for complexity assessment during stepwise ablation of atrial fibrillation

Introduction: Stepwise radiofrequency catheter ablation (step-CA) has become a treatment of choice for the restoration of sinus rhythm (SR) in patients (pts) with long-standing persistent atrial fibrillation (pers-AF). Its success rate appears limited as the amount of ablation to achieve long term SR is unknown. Recently, intracardiac organization indices (OI) of AF have been used to track the efficiency of step-CA, with limited success. Our study is aimed at developing new OIs based on the relationships between harmonic components of atrial activity from the surface ECG as a global assessment of AF complexity and organization during step-CA. Methods: 3 pts with pers-AF (age 62, AF duration 17 months) underwent a step-CA. An adaptive tracking algorithm was developed for estimating the instantaneous frequency of atrial activity on chest lead V1 (after QRST subtraction) and for extracting its fundamental and harmonic components. An adaptive organization index (AOI) was computed as the ratio between the power of the extracted components and the total power of the signal to evaluate the temporal evolution of AF oscillations. The variance of the phase difference (PD) between the fundamental and harmonic components was used for measuring AF regularity. Results: Step-CA terminated 2/3 pers-AF into flutter. Importantly, in the 2 terminated pts, the AOI did not show any significant change during the step-CA (from pre-ablation to CFAE), while the PD showed a gradual reduction suggestive of increased coupling between the fundamental and the 1st harmonic. See figure. Conclusions: The PD and the AOI as measurements of complexity from the surface ECG appear as promising methods for tracking the effect of step-CA on global AF organization. This, however, needs to be validated on a larger population.

Harmonic frequency tracking algorithm for predicting the success of pharmacological cardioversion of atrial fibrillation

Andréa Buttu, Lukas Kappenberger, Laurent Renaud Uldry, Jérôme Sébastien Van Zaen, Jean-Marc Vesin

Purpose: Non-invasively predicting the success of pharmacological cardioversion for patients with atrial fibrillation (AF) would be of great interest in clinical settings. In this study, an adaptive algorithm for tracking fundamental and harmonic components of atrial activity is proposed for extracting waveform features from surface ECG and discriminate patients with successful or failed cardioversion. Methods: 49 patients diagnosed with AF for whom pharmacological cardioversion was a success (15) or a failure (34) were studied. An adaptive tracking algorithm was applied to precordial ECG leads (V1-V6) for estimating the instantaneous frequency as well as extracting fundamental and first harmonic components with time-varying band-pass filters. Joint tracking on pairs of leads improved robustness. The phase difference between fundamental and harmonic signals was used as a measure of AF organization. Successful and failed cardioversions were classified with quadratic discriminant analysis based on the mean and variance of instantaneous AF frequency, and on the mean and variance of the phase difference slope. Results: The best selection of features for classifying successful and failed cardioversions achieved a correct rate of 81.6% with balanced sensitivity and specificity, using the mean instantaneous frequency and the mean and variance of phase difference slope estimated from leads V1 and V4. In this case, the negative predictive value was 90.3%, meaning that cardioversion failure could be predicted with high reliability. Conclusions: Adaptive tracking of AF harmonic components could potentially be used as a diagnostic tool for the assessment of future cardioversion efficacy. Indeed, an accurate prediction of future cardioversion failure could help tailoring treatments to appropriate options.

2011

Adaptive frequency tracking and application to biomedical signals

Yann Prudat

The main information of a signal resides in its frequency, its amplitude (or its power) and in their temporal evolution. Thus, a great number of methods for instantaneous frequency estimation have been proposed in the literature. Most of these algorithms use an adaptive filter-based (notch or bandpass filter) structure. In many applications, the information of interest is present in more than one signal. However, to our knowledge no algorithm able to track the frequency on several signals has been presented in the literature. Usually, frequency components are estimated and extracted on each signal independently. Artifacts or noise relative to a specific signal can thus disturb the frequency estimation process. Moreover, low amplitude components present in every signal (but non dominant) will not be estimated. The objective in the first part of this thesis is to develop different methods able to extend existing frequency tracking algorithm in order to improve the quality of the estimate, in terms of estimation variance and robustness with respect to noise. The proposed methods can be applied to algorithms using adaptive filters for the frequency estimation. For the multi-signal frequency tracking extension, these methods use the redundancy of information present in the signals under study. A first approach uses a unique filter for every signal. A set of weights is computed, depending on a measure of the estimate quality, and makes it possible to balance the influence of each signal on the tracking filter update. The second approach consists in using a different adaptive filter for each signal. A set of constraints links the central frequencies of each filter so that they are as similar as possible. Both methods yield frequency estimates more robust with respect to noise and more stable, without any decrease in estimation accuracy. For the harmonic frequency tracking, we propose a method using the information present in the harmonic component to improve the estimate of the fundamental frequency. The proposed methods also permit to extract the signal components corresponding to the estimated frequencies. These components are very useful for subsequent study. In the second part of this thesis, the algorithms developed in the first part are applied to biomedical signals. Two different applications are studied in this work : electrocardiograms and electroencephalograms. Firstly, a frequency tracking algorithm as well as its multi-signal extension are used to predict the success of electrical cardioversion attempts in patients suffering from atrial fibrillation. The instantaneous frequency is estimated using the algorithms and the corresponding signal component is extracted from electrocardiograms recorded prior to the attempt. With a few parameters computed on the estimated frequency and the corresponding signal component, we were able to predict the result of the cardioversion attempt on our database comprising 18 patients with a success rate of 94% for both algorithms. We think that this result can be very useful for helping the clinician to choose the appropriate therapy for atrial fibrillation management. The developed algorithms are also used to track the oscillatory components present in electroencephalograms. The performance of the basic algorithm is illustrated using single-trial electroencephalogram signals from a visual evoked potential experiment. The algorithm is used to track the gamma component (30-50 Hz). It is able to successfully track the spectral component in spite of the fact that large amplitude variations are present in the signal. A complex version of the multi-signal extension is also used to have an algorithm able to track multiple frequency components on multiple signals. The performance of this algorithm is also illustrated with single-trial electroencephalogram signals. It was shown to be able to correctly track up to four frequency components simultaneously. The quality of the estimation is improved using multiple lead signals.

EPFL2009

High pre-ablation ECG organization in long-standing persistent atrial fibrillation terminated within the left atrium

Andréa Buttu, Etienne Pruvot, Jérôme Sébastien Van Zaen, Jean-Marc Vesin

The outcome of stepwise catheter ablation (step-CA) for patients (pts) with long-standing atrial fibrillation (LS-pAF) is poorly predicted by classical organization indices (OI). Our study aims at developing new indices from pre-ablation ECG in order to identify the site of AF termination during step-CA. Methods: 23 consecutive pts (59±7y, AF duration 19±12m) underwent step-CA consisting in pulmonary veins isolation, left atrial (LA) defragmentation, and right atrial (RA) ablations for non terminated AF. Chest lead V6 was placed on pts’ back within the cardiac silhouette (V6b). QRST cancellation was performed on chest leads V1 and V6b. Using an adaptive harmonic frequency tracking scheme, two indices were computed to quantify the harmonic components of atrial activity: 1) PD, phase difference between the AF dominant frequency (DF) and its 1st harmonic measuring AF regularity, and 2) AOI, an adaptive OI tracking AF temporal evolution. Both indices were compared to two classical OIs: 1) SOI, ratio of the DF power and its 1st harmonic to the total power (3-15 Hz) and 2) AFCL, ECG AF cycle length. Results: LS-pAF was terminated in 83% (19/23) of the pts: 17 during LA (LT); 2 during RA ablation (RT); 4 were not terminated (NT). The figure shows that AOI on lead V1 (panel A) and PD on lead V6b (panel B) improved the separation between LT and RT/NT pts compared to SOI and AFCL respectively. AFCL was not significantly different between the two groups. Conclusions: Our results suggest that adaptive measures of AF organization computed from pre-ablation ECG perform better than classical indices for identifying pts whose AF will terminate during ablation within the LA. These finding are indicative of a higher baseline organization in these pts that could be used to select LS-pAF candidates for step-CA.

2013