Applications of Signal Analysis to Atrial Fibrillation

This work was supported by projects TEC2010–20633 from the Spanish Ministry of Science and Innovation and PPII11–0194–8121 from Junta de Comunidades de Castilla-La ManchaRieta Ibañez, JJ.; Alcaraz Martínez, R. (2013). Applications of Signal Analysis to Atrial Fibrillation. En Atrial Fibrillation - Mechanisms and Treatment. InTech. 155-180. https://doi.org/10.5772/5340915518

SPATIO-TEMPORAL VARIATION IN ACTIVATION INTERVALS DURING VENTRICULAR FIBRILLATION

Spatio-temporal variation in activation rates during ventricular fibrillation (VF)provides insight into mechanisms of sustained re-entry during VF. This study had three objectives related to spatio-temporal dynamics in activation rates during VF. The first objective was to quantify spatio-temporal variability in activation rates,that is, in dominant frequencies, computed from epicardial electrograms recorded during VF in swine. Results showed that temporally and spatially, dominant frequencies variedas much as 20% of the mean dominant frequency, and the mean dominant frequencies increased during first 30 sec of VF. These results suggest that activation rates are nonstationary during VF. The second objective of the study was to develop a new stimulation protocol for quantifying restitution of action potential duration (APD) by independently controlling diastolic intervals (DI). A property of cardiac cells that determines spatio-temporal variability in dominant frequencies is restitution of APD, which relates APD to the previous DI. Independent control of DI permits explicit determination of the role of memory in restitution. Restitution functions quantified using mathematical models of activation and our stimulation protocol, showed significant hysteresis. That is, for adiastolic interval, the action potential durations were as much as 15% longer during periods when the DI were decreasing than when the DI were increasing. We verified the feasibility of implementing our protocol experimentally in isolated and perfused rat hearts with action potentials recorded using floating glass microelectrodes. The third objective of our study was to verify that spatio-temporal variability in dominant frequencies during VF could be modified using spatially distributed pacing strength stimuli. Simulated VF was induced in 400x400 and 400x800 matrices of cells. Electrical function of cells was simulated using the Luo-Rudy model. Stimulators were arranged in the matrices such that there were 5 rows of line stimulators. Results showed that it was possible to modify activations in almost 54% of the area and to modify spatio-temporal variability in activation during VF into a desired pattern by the use of synchronized pacing from multiple sites. These results support further exploration of distributed stimulation approach for potential improvements in defibrillation therapy

Preoperative Prediction of Catheter Ablation Outcome in Persistent Atrial Fibrillation Patients through Spectral Organization Analysis of the Surface Fibrillatory Waves

Catheter ablation (CA) is a commonly used treatment for persistent atrial fibrillation (AF). Since its medium/long-term success rate remains limited, preoperative prediction of its outcome is gaining clinical interest to optimally select candidates for the procedure. Among predictors based on the surface electrocardiogram, the dominant frequency (DF) and harmonic exponential decay (g) of the fibrillatory waves ( f -waves) have reported promising but clinically insufficient results. Hence, the main goal of this work was to conduct a broader analysis of the f -wave harmonic spectral structure to improve CA outcome prediction through several entropy-based measures computed on different frequency bands. On a database of 151 persistent AF patients under radio-frequency CA and a follow-up of 9 months, the newly introduced parameters discriminated between patients who relapsed to AF and those who maintained SR at about 70%, which was statistically superior to the DF and approximately similar to g. They also provided complementary information to g through different combinations in multivariate models based on lineal discriminant analysis and report classification performance improvement of about 5%. These results suggest that the presence of larger harmonics and a proportionally smaller DF peak is associated with a decreased probability of AF recurrence after CA

Novel Entropy-Based Metrics for Long-Term Atrial Fibrillation Recurrence Prediction Following Surgical Ablation: Insights from Preoperative Electrocardiographic Analysis

[EN] Atrial fibrillation (AF) is a prevalent cardiac arrhythmia often treated concomitantly with other cardiac interventions through the Cox-Maze procedure. This highly invasive intervention is still linked to a long-term recurrence rate of approximately 35% in permanent AF patients. The aim of this study is to preoperatively predict long-term AF recurrence post-surgery through the analysis of atrial activity (AA) organization from non-invasive electrocardiographic (ECG) recordings. A dataset comprising ECGs from 53 patients with permanent AF who had undergone Cox-Maze concomitant surgery was analyzed. The AA was extracted from the lead V1 of these recordings and then characterized using novel predictors, such as the mean and standard deviation of the relative wavelet energy (RWEm and RWEs) across different scales, and an entropy-based metric that computes the stationary wavelet entropy variability (SWEnV). The individual predictors exhibited limited predictive capabilities to anticipate the outcome of the procedure, with the SWEnV yielding a classification accuracy (Acc) of 68.07%. However, the assessment of the RWEs for the seventh scale (RWEs7), which encompassed frequencies associated with the AA, stood out as the most promising individual predictor, with sensitivity (Se) and specificity (Sp) values of 80.83% and 67.09%, respectively, and an Acc of almost 75%. Diverse multivariate decision tree-based models were constructed for prediction, giving priority to simplicity in the interpretation of the forecasting methodology. In fact, the combination of the SWEnV and RWEs7 consistently outperformed the individual predictors and excelled in predicting post-surgery outcomes one year after the Cox-Maze procedure, with Se, Sp, and Acc values of approximately 80%, thus surpassing the results of previous studies based on anatomical predictors associated with atrial function or clinical data. These findings emphasize the crucial role of preoperative patient-specific ECG signal analysis in tailoring post-surgical care, enhancing clinical decision making, and improving long-term clinical outcomes.This research has received financial support from public grants PID2021-123804OB-I00, PID2021- 00X128525-IV0, and TED2021-130935B-I00 of the Spanish Government, 10.13039/501100011033, in conjunction with the European Regional Development Fund (EU), SBPLY/21/180501/000186, from Junta de Comunidades de Castilla-La Mancha, and AICO/2021/286 from Generalitat Valenciana. Pilar Escribano holds the 2020-PREDUCLM-15540 scholarship co-financed by the European Social Fund (ESF) operating program 2014 2020 of Castilla-La Mancha.Escribano, P.; Ródenas, J.; García, M.; Hornero, F.; Gracia-Baena, JM.; Alcaraz, R.; Rieta, JJ. (2024). Novel Entropy-Based Metrics for Long-Term Atrial Fibrillation Recurrence Prediction Following Surgical Ablation: Insights from Preoperative Electrocardiographic Analysis. Entropy. 26(1). https://doi.org/10.3390/e2601002826

Atrial Arrhythmogenic Substrates: The Role of Structure and Molecular Remodeling

Atrial tachyarrhythmias, specifically atrial flutter: AFl) and fibrillation: AF), affect over 2.2 million Americans, leading to more hospitalizations than any other cardiac arrhythmia. These arrhythmias are defined by the presence of reentrant circuits of excitation leading to high atrial rates and uncoordinated activation of the ventricles. The underlying mechanisms of AFl/AF have proven complex and, despite a century of research, no one effective treatment has been developed. Surgical ablation and pharmacological therapies are both fraught with risks and potential pro-arrhythmic side effects. Electrical cardioversion, on the other hand, is extremely effective in terminating these arrhythmias, but the high-energy shocks required for termination cause substantial pain to the patient. In this dissertation, we first investigate the underlying molecular and structural mechanisms of AF in two clinically-relevant models - the human and canine hearts. We identify structural and molecular substrates responsible for the generation and maintenance of AFl/AF. We then explore the application of a novel low-voltage defibrillation therapy to a rabbit model of atrial tachyarrhythmias and show significant reductions in the defibrillation threshold for both AF and AFl. We utilize a variety of experimental techniques, such as high throughput quantitative PCR, optical coherence tomography, and optical mapping. Only truly integrative approaches to arrhythmia research, combining a variety of experimental models and techniques, can continue to unravel the complexities of underlying molecular, structural, and electrophysiological mechanisms and develop effective, safe therapies, as we demonstrate in this dissertation with regards to atrial tachyarrhythmias

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

A multi-variate predictability framework to assess invasive cardiac activity and interactions during atrial fibrillation

Objective: This study introduces a predictability framework based on the concept of Granger causality (GC), in order to analyze the activity and interactions between different intracardiac sites during atrial fibrillation (AF). Methods: GC-based interactions were studied using a three-electrode analysis scheme with multi-variate autoregressive models of the involved preprocessed intracardiac signals. The method was evaluated in different scenarios covering simulations of complex atrial activity as well as endocardial signals acquired from patients. Results: The results illustrate the ability of the method to determine atrial rhythm complexity and to track and map propagation during AF. Conclusion: The proposed framework provides information on the underlying activation and regularity, does not require activation detection or postprocessing algorithms and is applicable for the analysis of any multielectrode catheter. Significance: The proposed framework can potentially help to guide catheter ablation interventions of AF

Central tendency measure and wavelet transform combined in the non-invasive analysis of atrial fibrillation recordings

Background Atrial fibrillation (AF) is the most common supraventricular arrhythmia in the clinical practice, being the subject of intensive research. Methods The present work introduces two different Wavelet Transform (WT) applications to electrocardiogram (ECG) recordings of patients in AF. The first one predicts spontaneous termination of paroxysmal AF (PAF), whereas the second one deals with the prediction of electrical cardioversion (ECV) outcome in persistent AF patients. In both cases, the central tendency measure (CTM) from the first differences scatter plot was applied to the AF wavelet decomposition. In this way, the wavelet coefficients vector CTM associated to the AF frequency scale was used to assess how atrial fibrillatory (f) waves variability can be related to AF events. Results Structural changes into the f waves can be assessed by combining WT and CTM to reflect atrial activity organization variation. This fact can be used to predict organization-related events in AF. To this respect, results in the prediction of PAF termination regarding sensitivity, specificity and accuracy were 100%, 91.67% and 96%, respectively. On the other hand, for ECV outcome prediction, 82.93% sensitivity, 90.91% specificity and 85.71% accuracy were obtained. Hence, CTM has reached the highest diagnostic ability as a single predictor published to date. Conclusions Results suggest that CTM can be considered as a promising tool to characterize non-invasive AF signals. In this sense, therapeutic interventions for the treatment of paroxysmal and persistent AF patients could be improved, thus, avoiding useless procedures and minimizing risks.This work was supported by the projects TEC2010-20633 from the Spanish Ministry of Science and Innovation and PPII11-0194-8121 and PII1C09-0036-3237 from Junta de Comunidades de Castilla-La Mancha.Alcaraz, R.; Rieta Ibañez, JJ. (2012). 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