Feasibility of visualizing higher regions of Shannon entropy in atrial fibrillation patients

Novel mapping techniques for rotor core detection using simulated intracardiac electrograms

BACKGROUND

Catheter ablation is associated with limited success rates in patients with persistent atrial fibrillation (AF). Currently, existing mapping systems fail to identify critical target sites for ablation. Recently, we proposed and validated several techniques (multiscale frequency [MSF], Shannon entropy [SE], kurtosis [Kt], and multiscale entropy [MSE]) to identify pivot point of rotors using ex-vivo optical mapping animal experiments. However, the performance of these techniques is unclear for the clinically recorded intracardiac electrograms (EGMs), due to the different nature of the signals.

OBJECTIVE

This study aims to evaluate the performance of MSF, MSE, SE, and Kt techniques to identify the pivot point of the rotor using unipolar and bipolar EGMs obtained from numerical simulations.

METHODS

Stationary and meandering rotors were simulated in a 2D human atria. The performances of new approaches were quantified by comparing the "true" core of the rotor with the core identified by the techniques. Also, the performances of all techniques were evaluated in the presence of noise, scar, and for the case of the multielectrode multispline and grid catheters.

RESULTS

Our results demonstrate that all the approaches are able to accurately identify the pivot point of both stationary and meandering rotors from both unipolar and bipolar EGMs. The presence of noise and scar tissue did not significantly affect the performance of the techniques. Finally, the core of the rotors was correctly identified for the case of multielectrode multispline and grid catheter simulations.

CONCLUSION

The core of rotors can be successfully identified from EGMs using novel techniques; thus, providing motivation for future clinical implementations.

Entropy Mapping Approach for Functional Reentry Detection in Atrial Fibrillation: An In-Silico Study

Catheter ablation of critical electrical propagation sites is a promising tool for reducing the recurrence of atrial fibrillation (AF). The spatial identification of the arrhythmogenic mechanisms sustaining AF requires the evaluation of electrograms (EGMs) recorded over the atrial surface. This work aims to characterize functional reentries using measures of entropy to track and detect a reentry core. To this end, different AF episodes are simulated using a 2D model of atrial tissue. Modified Courtemanche human action potential and Fenton–Karma models are implemented. Action potential propagation is modeled by a fractional diffusion equation, and virtual unipolar EGM are calculated. Episodes with stable and meandering rotors, figure-of-eight reentry, and disorganized propagation with multiple reentries are generated. Shannon entropy (ShEn), approximate entropy (ApEn), and sample entropy (SampEn) are computed from the virtual EGM, and entropy maps are built. Phase singularity maps are implemented as references. The results show that ApEn and SampEn maps are able to detect and track the reentry core of rotors and figure-of-eight reentry, while the ShEn results are not satisfactory. Moreover, ApEn and SampEn consistently highlight a reentry core by high entropy values for all of the studied cases, while the ability of ShEn to characterize the reentry core depends on the propagation dynamics. Such features make the ApEn and SampEn maps attractive tools for the study of AF reentries that persist for a period of time that is similar to the length of the observation window, and reentries could be interpreted as AF-sustaining mechanisms. Further research is needed to determine and fully understand the relation of these entropy measures with fibrillation mechanisms other than reentries.

Analytical approaches for myocardial fibrillation signals

Atrial and ventricular fibrillation are complex arrhythmias, and their underlying mechanisms remain widely debated and incompletely understood. This is partly because the electrical signals recorded during myocardial fibrillation are themselves complex and difficult to interpret with simple analytical tools. There are currently a number of analytical approaches to handle fibrillation data. Some of these techniques focus on mapping putative drivers of myocardial fibrillation, such as dominant frequency, organizational index, Shannon entropy and phase mapping. Other techniques focus on mapping the underlying myocardial substrate sustaining fibrillation, such as voltage mapping and complex fractionated electrogram mapping. In this review, we discuss these techniques, their application and their limitations, with reference to our experimental and clinical data. We also describe novel tools including a new algorithm to map microreentrant circuits sustaining fibrillation.

Information Theory and Atrial Fibrillation (AF): A Review

Atrial Fibrillation (AF) is the most common cardiac rhythm disorder seen in hospitals and in general practice, accounting for up to a third of arrhythmia related hospitalizations. Unfortunately, AF treatment is in practice complicated by the lack of understanding of the fundamental mechanisms underlying the arrhythmia, which makes detection of effective ablation targets particularly difficult. Various approaches to AF mapping have been explored in the hopes of better pinpointing these effective targets, such as Dominant Frequency (DF) analysis, complex fractionated electrograms (CFAE) and unipolar reconstruction (FIRM), but many of these methods have produced conflicting results or require further investigation. Exploration of AF using information theoretic-based approaches may have the potential to provide new insights into the complex system dynamics of AF, whilst also providing the benefit of being less reliant on empirically derived definitions in comparison to alternate mapping approaches. This work provides an overview of information theory and reviews its applications in AF analysis, with particular focus on AF mapping. The works discussed in this review demonstrate how understanding AF from a signal property perspective can provide new insights into the arrhythmic phenomena, which may have valuable clinical implications for AF mapping and ablation in the future.

Improved Multiscale Entropy Technique with Nearest-Neighbor Moving-Average Kernel for Nonlinear and Nonstationary Short-Time Biomedical Signal Analysis

Analysis of biomedical signals can yield invaluable information for prognosis, diagnosis, therapy evaluation, risk assessment, and disease prevention which is often recorded as short time series data that challenges existing complexity classification algorithms such as Shannon entropy (SE) and other techniques. The purpose of this study was to improve previously developed multiscale entropy (MSE) technique by incorporating nearest-neighbor moving-average kernel, which can be used for analysis of nonlinear and non-stationary short time series physiological data. The approach was tested for robustness with respect to noise analysis using simulated sinusoidal and ECG waveforms. Feasibility of MSE to discriminate between normal sinus rhythm (NSR) and atrial fibrillation (AF) was tested on a single-lead ECG. In addition, the MSE algorithm was applied to identify pivot points of rotors that were induced in ex vivo isolated rabbit hearts. The improved MSE technique robustly estimated the complexity of the signal compared to that of SE with various noises, discriminated NSR and AF on single-lead ECG, and precisely identified the pivot points of ex vivo rotors by providing better contrast between the rotor core and the peripheral region. The improved MSE technique can provide efficient complexity analysis of variety of nonlinear and nonstationary short-time biomedical signals.

Novel Quantitative Analytical Approaches for Rotor Identification and Associated Implications for Mapping

GOAL

Clinical studies identifying rotors and confirming these sites for ablation in treating cardiac arrhythmias have had inconsistent results with the currently available analysis techniques. The aim of this study is to evaluate four new signal analysis approaches-multiscale frequency (MSF), Shannon entropy (SE), Kurtosis (Kt), and multiscale entropy (MSE)-in their ability to identify the pivot point of rotors.

METHODS

Optical mapping movies of ventricular tachycardia were used to evaluate the performance and robustness of SE, Kt, MSF, and MSE techniques with respect to several clinical limitations: decreased time duration, reduced spatial resolution, and the presence of meandering rotors. To quantitatively assess the robustness of the four techniques, results were compared to the "true" rotor(s) identified using optical mapping-based phase maps.

RESULTS

The results demonstrate that MSF, Kt, and MSE accurately identified both stationary and meandering rotors. In addition, these techniques remained accurate under simulated clinical limitations: shortened electrogram duration and decreased spatial resolution. Artifacts mildly affected the performance of MSF, Kt, and MSE, but strongly impacted the performance of SE.

CONCLUSION

These results motivate further validation using intracardiac electrograms to see if these approaches can map rotors in a clinical setting and whether they apply to more complex arrhythmias including atrial or ventricular fibrillation.

SIGNIFICANCE

New techniques providing more accurate rotor localization could improve characterization of arrhythmias and, in turn, offer a means to accurately evaluate whether rotor ablation is a viable and effective treatment for chaotic cardiac arrhythmias.

Kurtosis as a statistical approach to identify the pivot point of the rotor

Atrial fibrillation (AF) is the most common sustained cardiac arrhythmia that causes stroke affecting more than 2.3 million people in the US. Catheter ablation to terminate AF is successful for paroxysmal AF but suffers limitations with persistent AF patients as current mapping methods cannot identify AF active substrates outside of pulmonary vein region. In this work, we developed a novel Kurtosis based mapping technique that can accurately identify pivot points of the rotors that were induced in ex-vivo isolated rabbit heart. The results indicate that the chaotic nature of rotor pivot point results in higher Kurtosis compared to the periphery thereby enabling its accurate identification. Our results suggest that Kurtosis technique can be further applied to intra-atrial electrograms from AF patients with rotors to accurately identify the rotor pivot point by generating 3-dimensional (3D) patient-specific Kurtosis maps. Validation of this new Kurtosis based mapping technology is required through clinical studies with both paroxysmal and persistent AF patient data.

Novel approaches for quantitative electrogram analysis for intraprocedural guidance for catheter ablation: A case of a patient with persistent atrial fibrillation

PURPOSE

Atrial fibrillation (AF) is the most common sustained cardiac arrhythmia that causes stroke affecting more than 2.3 million people in the US and is increasing in prevalence due to ageing population causing a new global epidemic. Catheter ablation with pulmonary vein isolation (PVI) to terminate AF is successful for paroxysmal AF but suffers limitations with persistent AF patients as current mapping methods cannot identify AF active substrates outside of PVI region. Recent evidences in the mechanistic understating of AF pathophysiology suggest that ectopic activity, localized re-entrant circuit with fibrillatory propagation and multiple circuit re-entries may all be involved in human AF. The authors developed novel electrogram analysis methods and validated using optical mapping data from isolated rabbit hearts to accurately identify rotor pivot points. The purpose of this study was to assess the feasibility of generating patient-specific 3D maps for intraprocedural guidance for catheter ablation using intracardiac electrograms from a persistent AF patient using novel electrogram analysis methods.

METHODS

A persistent AF patient with clinical appointment for AF ablation was recruited for this study with IRB approval. 1055 electrograms throughout the left and right atrium were obtained for offline analysis with the novel approaches such as multiscale entropy, multiscale frequency, recurrence period density entropy, kurtosis and empirical mode decomposition to generate patient specific 3D maps. 3D Shannon Entropy, Renyi Entropy and Dominant frequency maps were also generated for comparison purposes along with local activation time and complex fractionated electrogram analysis maps.

RESULTS

Patient specific 3D maps were obtained for each of the different approach. The 3D maps indicate potential active sites outside the PVI region. However, presence of rotors cannot be confirmed and validation of these approaches is required on a larger dataset.

CONCLUSIONS

Conventional catheter mapping system can be used for generating patient specific 3D maps with short time series analysis using the novel approaches.