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

The role of the Cx43/Cx45 gap junction voltage gating on wave propagation and arrhythmogenic activity in cardiac tissue

Gap junctions (GJs) formed of connexin (Cx) protein are the main conduits of electrical signals in the heart. Studies indicate that the transitional zone of the atrioventricular (AV) node contains heterotypic Cx43/Cx45 GJ channels which are highly sensitive to transjunctional voltage (Vj). To investigate the putative role of Vj gating of Cx43/Cx45 channels, we performed electrophysiological recordings in cell cultures and developed a novel mathematical/computational model which, for the first time, combines GJ channel Vj gating with a model of membrane excitability to simulate a spread of electrical pulses in 2D. Our simulation and electrophysiological data show that Vj transients during the spread of cardiac excitation can significantly affect the junctional conductance (gj) of Cx43/Cx45 GJs in a direction- and frequency-dependent manner. Subsequent simulation data indicate that such pulse-rate-dependent regulation of gj may have a physiological role in delaying impulse propagation through the AV node. We have also considered the putative role of the Cx43/Cx45 channel gating during pathological impulse propagation. Our simulation data show that Vj gating-induced changes in gj can cause the drift and subsequent termination of spiral waves of excitation. As a result, the development of fibrillation-like processes was significantly reduced in 2D clusters, which contained Vj-sensitive Cx43/Cx45 channels.

Complexity analysis of electrical activity during endocardial and epicardial biventricular mapping of ventricular fibrillation

BACKGROUND

Ventricular fibrillation (VF) is a lethal cardiac arrhythmia that is a significant cause of sudden cardiac death. Comprehensive studies of spatiotemporal characteristics of VF in situ are difficult to perform with current mapping systems and catheter technology.

OBJECTIVE

The goal of this study was to develop a computational approach to characterize VF using a commercially available technology in a large animal model. Prior data suggests that characterization of spatiotemporal organization of electrical activity during VF can be used to provide better mechanistic understanding and potential ablation targets to modify VF and its substrate. We therefore evaluated intracardiac electrograms during biventricular mapping of the endocardium (ENDO) and epicardium (EPI) in acute canine studies.

METHODS

To develop thresholds for organized and disorganized activity, a linear discriminant analysis (LDA)-based approach was performed to the known organized and disorganized activities recorded in ex vivo Langendorff-perfused rat and rabbit hearts using optical mapping experiments. Several frequency- and time-domain approaches were used as individual and paired features to identify the optimal thresholds for the LDA approach. Subsequently, VF was sequentially mapped in 4 canine hearts, using the CARTO mapping system with a multipolar mapping catheter in the ENDO left and right ventricles and EPI to capture the progression of VF at 3 discrete post-induction time intervals: VF period 1 (just after induction of VF to 15 min), VF period 2 (15 to 30 min), and VF period 3 (30 to 45 min). The developed LDA model, cycle lengths (CL), and regularity indices (RI) were applied to all recorded intracardiac electrograms to quantify the spatiotemporal organization of VF in canine hearts.

RESULTS

We demonstrated the presence of organized activity in the EPI as VF progresses, in contrary to the ENDO, where the activity stays disorganized. The shortest CL always occurred in the ENDO, especially the RV, indicating a faster VF activity. The highest RI was found in the EPI in all hearts for all VF stages, indicating spatiotemporal consistency of RR intervals.

CONCLUSION

We identified electrical organization and spatiotemporal differences throughout VF in canine hearts from induction to asystole. Notably, the RV ENDO is characterized by a high level of disorganization and faster VF frequency. In contrast, EPI has a high spatiotemporal organization of VF and consistently long RR intervals.

A novel approach for quantitative electrogram analysis for driver identification: Implications for ablation in persistent atrial fibrillation

Objective

This study sought to study the feasibility, efficacy, and safety of using multiscale entropy (MSE) analysis to guide catheter ablation for persistent atrial fibrillation (PsAF) and predict ablation outcomes.

Methods

We prospectively enrolled 108 patients undergoing initial ablation for PsAF. MSE was calculated based on bipolar intracardiac electrograms (iEGMs) to measure the dynamical complexity of biological signals. The iEGMs data were exported after pulmonary vein isolation (PVI), then calculated in a customed platform, and finally re-annotated into the CARTO system. After PVI, regions of the highest mean MSE (mMSE) values were ablated in descending order until AF termination, or three areas had been ablated.

Results

Baseline characteristics were evenly distributed between the AF termination (n = 38, 35.19%) and the non-termination group. The RA-to-LA mean MSE (mMSE) gradient demonstrated a positive gradient in the non-termination group and a negative gradient in the termination group (0.105 ± 0.180 vs. −0.235 ± 0.256, P < 0.001). During a 12-month follow-up, 29 patients (26.9%) had arrhythmia recurrence after single ablation, and 18 of them had AF (62.1%). The termination group had lower rates of arrhythmia recurrence (15.79 vs. 32.86%, Log-Rank P = 0.053) and AF recurrence (10.53 vs. 20%, Log-Rank P = 0.173) after single ablation and a lower rate of arrhythmia recurrence (7.89 vs. 27.14%, Log-Rank P = 0.018) after repeated ablation. Correspondingly, subjects with negative RA-to-LA mMSE gradient had lower incidences of arrhythmia (16.67 vs. 35%, Log-Rank P = 0.028) and AF (16.67 vs. 35%, Log-Rank P = 0.032) recurrence after single ablation and arrhythmia recurrence after repeated ablation (12.5 vs. 26.67%, Log-Rank P = 0.062). Marginal peri-procedural safety outcomes were observed.

Conclusion

MSE analysis-guided driver ablation in addition to PVI for PsAF could be feasible, efficient, and safe. An RA < LA mMSE gradient before ablation could predict freedom from arrhythmia. The RA-LA MSE gradient could be useful for guiding ablation strategy selection.

Similarity Score for the Identification of Active Sites in Patients With Atrial Fibrillation

Background

Atrial fibrillation (AF) is the most common cardiac arrhythmia and precursor to other cardiac diseases. Catheter ablation is associated with limited success rates in patients with persistent AF. Currently, existing mapping systems fail to identify critical target sites for ablation. Recently, we proposed and validated several individual techniques, such as dominant frequency (DF), multiscale frequency (MSF), kurtosis (Kt), and multiscale entropy (MSE), to identify active sites of arrhythmias using simulated intracardiac electrograms (iEGMs). However, the individual performances of these techniques to identify arrhythmogenic substrates are not reliable.

Objective

This study aimed to develop a similarity score using various iEGM analysis techniques to more accurately identify the spatial location of active sites of arrhythmia in patients with AF.

Methods

Clinical bipolar iEGMs were obtained from patients with AF who underwent either successful (m = 4) or unsuccessful (m = 4) catheter ablation. A similarity score (0–3) was developed via the earth mover’s distance (EMD) approach based on a combination of DF, MSF, MSE, and Kt techniques.

Results

Individual techniques successfully discriminated between successful and unsuccessful AF ablation patients but were not reliable in identifying active spatial sites of AF. However, the proposed similarity score was able to pinpoint the spatial sites with high values (active AF sites) that were observed only in patients with unsuccessful AF termination, suggesting that these active sites were missed during the ablation procedure.

Conclusion

Arrhythmogenic substrates with abnormal electrical activity are identified in patients with unsuccessful AF termination after catheter ablation, suggesting clinical efficacy of similarity score.

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.

Optimizing Multiscale Entropy Approach for Rotor Core Identification using Simulated Intracardiac Electrograms

Atrial Fibrillation (AF) is most common sustained cardiac arrhythmia and a precursor to many fatal cardiac conditions. Catheter ablation, which is a minimally invasive treatment, is associated with limited success rates in patients with persistent AF. Rotors are believed to maintain AF and core of rotors are considered to be robust targets for ablation. Recently, multiscale entropy (MSE) was proposed to identify the core of rotors in ex-vivo rabbit hearts. However, MSE technique is sensitive to intrinsic parameters, such as scale factor and template dimension, that may lead to an imprecise estimation of entropy measures. The purpose of this research is optimize MSE approach to improve its accuracy and sensitivity in rotor core identification using simulated EGMs from human atrial model. Specifically, we have identified the optimal time scale factor (τopt) and optimal template dimension (Τopt) that are needed for efficient rotor core identification. The τopt was identified to be 10, using a convergence graph, and the Τopt (~20 ms) remained the same at different sampling rates, indicating that optimized MSE will be efficient in identifying core of the rotor irrespective of the signal acquisition system.

[Research progress on multiscale entropy algorithm and its application in neural signal analysis]

Changes in the intrinsic characteristics of brain neural activities can reflect the normality of brain functions. Therefore, reliable and effective signal feature analysis methods play an important role in brain dysfunction and relative diseases early stage diagnosis. Recently, studies have shown that neural signals have nonlinear and multi-scale characteristics. Based on this, researchers have developed the multi-scale entropy (MSE) algorithm, which is considered more effective when analyzing multi-scale nonlinear signals, and is generally used in neuroinformatics. The principles and characteristics of MSE and several improved algorithms base on disadvantages of MSE were introduced in the article. Then, the applications of the MSE algorithm in disease diagnosis, brain function analysis and brain-computer interface were introduced. Finally, the challenges of these algorithms in neural signal analysis will face to and the possible further investigation interests were discussed.

Towards the Development of Nonlinear Approaches to Discriminate AF from NSR Using a Single-Lead ECG

Paroxysmal atrial fibrillation (Paro. AF) is challenging to identify at the right moment. This disease is often undiagnosed using currently existing methods. Nonlinear analysis is gaining importance due to its capability to provide more insight into complex heart dynamics. The aim of this study is to use several recently developed nonlinear techniques to discriminate persistent AF (Pers. AF) from normal sinus rhythm (NSR), and more importantly, Paro. AF from NSR, using short-term single-lead electrocardiogram (ECG) signals. Specifically, we adapted and modified the time-delayed embedding method to minimize incorrect embedding parameter selection and further support to reconstruct proper phase plots of NSR and AF heart dynamics, from MIT-BIH databases. We also examine information-based methods, such as multiscale entropy (MSE) and kurtosis (Kt) for the same purposes. Our results demonstrate that embedding parameter time delay (τ), as well as MSE and Kt values can be successfully used to discriminate between Pers. AF and NSR. Moreover, we demonstrate that τ and Kt can successfully discriminate Paro. AF from NSR. Our results suggest that nonlinear time-delayed embedding method and information-based methods provide robust discriminating features to distinguish both Pers. AF and Paro. AF from NSR, thus offering effective treatment before suffering chaotic Pers. AF.