Therapeutic Strategies for the Treatment of Atrial Fibrillation:New Insights from Biophysical Modeling and Signal Processing

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

Estimating the time scale and anatomical location of atrial fibrillation spontaneous termination in a biophysical model

Due to their transient nature, spontaneous terminations of atrial fibrillation (AF) are difficult to investigate. Apparently, confounding experimental findings about the time scale of this phenomenon have been reported, with values ranging from 1s to 1min. We propose a biophysical modeling approach to study the mechanisms of spontaneous termination in two models of AF with different levels of dynamical complexity. 8s preceding spontaneous terminations were studied and the evolution of cycle length and wavefront propagation were documented to assess the time scale and anatomical location of the phenomenon. Results suggest that termination mechanisms are dependent on the underlying complexity of AF. During simulated AF of low complexity, the total process of spontaneous termination lasted 3,200ms and was triggered in the left atrium 800ms earlier than in the right atrium. The last fibrillatory activity was observed more often in the right atrium. These asymmetric termination mechanisms in both time and space were not observed during spontaneous terminations of complex AF simulations, which showed less predictable termination patterns lasting only 1,600ms. This study contributes to the interpretation of previous clinical observations, and illustrates how computer modeling provides a complementary approach to study the mechanisms of cardiac arrhythmia

Estimating the time scale and anatomical location of atrial fibrillation spontaneous termination in a biophysical model

Due to their transient nature, spontaneous terminations of atrial fibrillation (AF) are difficult to investigate. Apparently, confounding experimental findings about the time scale of this phenomenon have been reported, with values ranging from 1 s to 1 min. We propose a biophysical modeling approach to study the mechanisms of spontaneous termination in two models of AF with different levels of dynamical complexity. 8 s preceding spontaneous terminations were studied and the evolution of cycle length and wavefront propagation were documented to assess the time scale and anatomical location of the phenomenon. Results suggest that termination mechanisms are dependent on the underlying complexity of AF. During simulated AF of low complexity, the total process of spontaneous termination lasted 3,200 ms and was triggered in the left atrium 800 ms earlier than in the right atrium. The last fibrillatory activity was observed more often in the right atrium. These asymmetric termination mechanisms in both time and space were not observed during spontaneous terminations of complex AF simulations, which showed less predictable termination patterns lasting only 1,600 ms. This study contributes to the interpretation of previous clinical observations, and illustrates how computer modeling provides a complementary approach to study the mechanisms of cardiac arrhythmias

Study of the effect of rapid pacing of atrial fibrillation on the non-paced atrium

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<0.001) reduction in #WF and increase in ET and OI in the paced atrium. In the non-paced atrium, AFCL only slightly decreased while the effect on #WF was the opposite to what was observed in the paced atrium (increase) and ET did not change. Interestingly, the effect on OI was different when pacing from the LA (decrease in the non-paced atrium compared to no pacing) or the RA (increase). Conclusions: The effect of AF rapid pacing on the non-paced atrium showed an acceleration of AF with an increased number of wavefronts. However, the effect on AF organization was dependent on the pacing site, which could explain the contradictory results reported in humans

Optimization of antitachycardia pacing protocols applied to atrial fibrillation: insights from a biophysical model

We present a model-based systematic study of antitachycardia pacing protocols applied to atrial fibrillation, focusing on the ability to achieve and maintain capture during pacing, as a function of both pacing site and period. We observed that pacing sites located away from anatomical obstacles led to faster and more robust capture. Moreover, after comparing burst and ramp pacing, our results indicate that in order to get capture it is necessary to pace at a fixed optimal period over a sufficient long time

Optimizing Local Capture of Atrial Fibrillation by Rapid Pacing: Study of the Influence of Tissue Dynamics

While successful termination by pacing of organized atrial tachycardias has been observed in patients, rapid pacing of AF can induce a local capture of the atrial tissue but in general no termination. The purpose of this study was to perform a systematic evaluation of the ability to capture AF by rapid pacing in a biophysical model of the atria with different dynamics in terms of conduction velocity (CV) and action potential duration (APD). Rapid pacing was applied during 30 s at five locations on the atria, for pacing cycle lengths in the range 60-110% of the mean AF cycle length (AFCL(mean)). Local AF capture could be achieved using rapid pacing at pacing sites located distal to major anatomical obstacles. Optimal pacing cycle lengths were found in the range 74-80% AFCL(mean) (capture window width: 14.6 +/- A 3% AFCL(mean)). An increase/decrease in CV or APD led to a significant shrinking/stretching of the capture window. Capture did not depend on AFCL, but did depend on the atrial substrate as characterized by an estimate of its wavelength, a better capture being achieved at shorter wavelengths. This model-based study suggests that a proper selection of the pacing site and cycle length can influence local capture results and that atrial tissue properties (CV and APD) are determinants of the response to rapid pacing

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

This study presents an automatic diagnostic method for the discrimination between persistent and long-standing atrial fibrillation (AF) based on the surface electrocardiogram (ECG). Standard 12-lead ECG recordings were acquired in 53 patients with either persistent (N 20) or long-standing AF (N 33), the latter including both long-standing persistent and permanent AF. A combined frequency analysis of multiple ECG leads followed by the computation of standard complexity measures provided a method for the quantification of spatiotemporal AF organization. All possible pairs of precordial ECG leads were analysed by this method and resulting organization measures were used for automatic classification of persistent and long-standing AF signals. Correct classification rates of 84.9 were obtained, with a predictive value for long-standing AF of 93.1. Spatiotemporal organization as measured in lateral precordial leads V5 and V6 was shown to be significantly lower during long-standing AF than persistent AF, suggesting that time-related alterations in left atrial electrical activity can be detected in the ECG. Accurate discrimination between persistent and long-standing AF based on standard surface recordings was demonstrated. This information could contribute to optimize the management of sustained AF, permitting appropriate therapeutic decisions and thereby providing substantial clinical cost savings

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

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