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Termination of persistent atrial fibrillation (AF) is a valuable ablation endpoint but is difficult to anticipate. We evaluated whether temporal and spatial indices of AF regularization predict intraprocedural AF termination and outcome.The purpose of this study was to test whether temporospatial organization of AF after pulmonary vein isolation (PVI) predicts whether subsequent stepwise ablation will terminate persistent AF or predict outcome.In 75 patients with persistent AF, we measured AF cycle length (AFCL), temporal regularity index (TRI, a spectral measure of timing regularity), and spatial regularity index (SRI, cycle-to-cycle variations in spatial vector) between right atrial appendage and proximal and distal coronary sinus before and during stepwise ablation to the endpoint of AF termination.AF termination was achieved in 59 patients (79%) by ablation. AF terminated during PVI in 11 patients, who were excluded from analysis. In the remaining 48 patients, TRI and SRI increased during stepwise ablation, as compared with 16 patients without termination (P.05). AFCL was prolonged in both groups. From receiver operating characteristics analysis of the first 22 patients (training set), a post-PVI TRI increase predicted AF termination in the latter 42 patients (test set) with a positive predictive value of 96%, negative predictive value of 53%, sensitivity of 71%, and specificity of 91%. Results were similar for SRI. After 36 months, higher arrhythmia-free outcome was observed in patients in whom PVI caused temporospatial regularization in AF.Temporal and spatial regularization of persistent AF after PVI identifies patients in whom stepwise ablation subsequently terminates AF and prevents recurrence.

Atrial fibrillation is the most common cardiac rhythm disorder encountered in clinical practice, often leading to severe complications such as heart failure and stroke. This arrhythmia, increasing in prevalence with age, already affects several millions of people in the United States, with a rising occurrence of the disease during the past two decades. In spite of these warning signals, atrial fibrillation is still difficult to treat, because basic mechanisms of the arrhythmia remain poorly understood and current treatments are therefore based on empirical considerations. The future of therapeutic solutions for the treatment of complex diseases such as atrial fibrillation relies on a strong collaboration between medicine, biology and engineering. Only through such synergies will efficient monitoring, diagnostic and therapeutic devices be created. The goal of the present thesis was to adopt this multidisciplinary approach, and develop new strategies for atrial fibrillation therapy using both computer modeling and advanced signal processing methods. Biophysical modeling is a practical and ethically interesting approach to develop innovative therapies, since physiological phenomena of interest are reproduced numerically and the resulting framework is then used with full repeatability to explore mechanisms and test treatments. A model of the human atria, that was developed in our group, was used to simulate atrial fibrillation and perform mechanistic and therapeutic investigations. In a first study, computer simulations were used to observe spontaneous terminations of two models of atrial fibrillation corresponding to different developmental stages of the arrhythmia. Dynamical parameters were observed during several seconds prior to termination in order to describe the underlying mechanisms of this natural phenomenon, showing that different levels of fibrillation complexity led to different termination patterns. The mechanisms highlighted by the study were successfully compared to those described in the existing literature and could suggest interesting guidelines to better investigate spontaneous terminations of atrial fibrillation in experimental and clinical settings. Moreover, a more precise understanding of the natural extinction of atrial fibrillation will certainly be crucial for future therapy developments. The potential of rapid low-energy pacing for artificially terminating atrial fibrillation was also thoroughly investigated. First, the possibility to entrain and thereby control fibrillating atrial activity by rapid pacing was studied in a systematic manner. Results showed that optimized pacing parameters provided sustained entrainment of electrical activity, although total extinction of atrial fibrillation was never observed. The ability to control atrial activity by pacing was also shown to depend on specific properties of the atrial tissue, showing that patients with atrial fibrillation may not all respond in the same way to pacing treatments. Finally, this study suggested different guidelines for the development of pace-termination algorithms for atrial fibrillation. Based on these results, a new pacing sequence for the automatic termination of atrial fibrillation was designed, implemented and tested in the biophysical model. The pacing protocol comprised two distinct phases involving a succession of rapid and slow pacing stimulations. The results of the tests suggest that this pacing scheme could represent an alternative to current treatments of atrial fibrillation, and could easily be implemented in patients who already have an indication for pacing. Advanced signal processing techniques were also used in this thesis to analyze real cardiac signals and develop new diagnosis tools. Multivariate spectral analysis and complexity measures were combined to develop an automatic method able to describe subtle changes in atrial fibrillation organization as measured by non-invasive ECG recordings. Accurate discrimination between persistent and permanent AF was shown possible, and potential applications in clinical settings to optimize patient management were demonstrated. Collectively, the results of this thesis show that major public health issues such as atrial fibrillation can strongly benefit from the contribution of biomedical engineering. The modeling and signal processing approaches used in the present dissertation proved effective and promising, and synergies between clinicians and scientists will definitely be at the basis of future therapies.

In this chapter, we propose a novel method for tracking oscillatory components in EEG signals by means of an adaptive filter-bank. The specificity of our tracking algorithm is to maximize the oscillatory behavior of its output rather than its spectral power, which shows interesting properties for the observation of neuronal oscillations. Besides, the structure of the filter-bank allows for efficiently tracking multiple frequency components perturbed by noise, therefore providing a good framework for EEG spectral analysis. Moreover, our algorithm can be generalized to multivariate data analysis, allowing the simultaneous investigation of several EEG sensors. Thus, a more precise extraction of spectral information can be obtained from the EEG signal under study. After a short introduction, we present our algorithm as well as synthetic examples illustrating its potential. Then, the performance of the method on real EEG signals is presented for the tracking of both single oscillatory component and multiple components. Finally, future lines of improvement as well as areas of applications are discussed.

Purpose: Experimental studies showed that local capture of atrial fibrillation (AF) by rapid pacing was possible in humans. However, contradictory observations were reported on its effect at distant atrial sites. The present model-based study investigated the effect of rapid pacing on the paced and the non-paced atria. Methods: A biophysical model of AF based on a geometry from human MRI and a membrane kinetics model was used. Rapid AF pacing was applied during 30s in right atrial (RA) free wall or left atrial (LA) appendage at optimal pacing cycle lengths based on previous studies (RA:76 ms, LA:77ms). The pacing effect was characterized by measuring 256 electrograms evenly located in RA and LA, from which the following values were computed: AF cycle length (AFCL), number of wavefronts (#WF), percentage of excited tissue (ET) and AF organization index (OI) assessing spectral regularity and ranging from 0 to 1. Results: Pacing successfully induced local AF capture in the paced atrium with an AFCL close to the pacing cycle length. Local capture was accompanied by a significant (p

Purpose: Distinguishing persistent from permanent atrial fibrillation (AF) in a reliable way would improve AF patient management, as cardioversion options could be considered appropriately. In this study, complexity measures based on the combined analysis of multiple surface ECG leads were tested to classify persistent or permanent AF. Methods: 53 patients diagnosed with persistent (N=20) or permanent (N=33) AF were studied. Multidimensional spectral analysis was performed on each possible combination of precordial ECG leads (V1 to V6) using the so-called spectral envelope. This method detects and emphasizes any oscillatory component common to several ECG leads. On resulting spectra, multivariate organization index (MOI) and multivariate spectral entropy (MSE) were computed to assess AF organization. These measures were used to classify persistent and permanent AF via quadratic discriminant analysis. Multivariate methods were compared to their univariate counterparts, i.e. organization index (OI) and spectral entropy (SE) computed on a single ECG lead. Results: Multivariate complexity measures were able to accurately distinguish persistent and permanent AF with correct rates up to 88.7%. The predictive value for permanent AF was 96.6%. MOI was significantly lower in patients with permanent AF compared to patients with persistent AF (p

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.