Quantitative techniques for analyzing high-resolution cardiac-mapping data

A 3D-CNN with temporal-attention block to predict the recurrence of atrial fibrillation based on body-surface potential mapping signals

Catheter ablation has become an important treatment for atrial fibrillation (AF), but its recurrence rate is still high. The aim of this study was to predict AF recurrence using a three-dimensional (3D) network model based on body-surface potential mapping signals (BSPMs). BSPMs were recorded with a 128-lead vest in 14 persistent AF patients before undergoing catheter ablation (Maze-IV). The torso geometry was acquired and meshed by point cloud technology, and the BSPM was interpolated into the torso geometry by the inverse distance weighted (IDW) method to generate the isopotential map. Experiments show that the isopotential map of BSPMs can reflect the propagation of the electrical wavefronts. The 3D isopotential sequence map was established by combining the spatial–temporal information of the isopotential map; a 3D convolutional neural network (3D-CNN) model with temporal attention was established to predict AF recurrence. Our study proposes a novel attention block that focuses the characteristics of atrial activations to improve sampling accuracy. In our experiment, accuracy (ACC) in the intra-patient evaluation for predicting the recurrence of AF was 99.38%. In the inter-patient evaluation, ACC of 3D-CNN was 81.48%, and the area under the curve (AUC) was 0.88. It can be concluded that the dynamic rendering of multiple isopotential maps can not only comprehensively display the conduction of cardiac electrical activity on the body surface but also successfully predict the recurrence of AF after CA by using 3D isopotential sequence maps.

Techniques for automated local activation time annotation and conduction velocity estimation in cardiac mapping

Measurements of cardiac conduction velocity provide valuable functional and structural insight into the initiation and perpetuation of cardiac arrhythmias, in both a clinical and laboratory context. The interpretation of activation wavefronts and their propagation can identify mechanistic properties of a broad range of electrophysiological pathologies. However, the sparsity, distribution and uncertainty of recorded data make accurate conduction velocity calculation difficult. A wide range of mathematical approaches have been proposed for addressing this challenge, often targeted towards specific data modalities, species or recording environments. Many of these algorithms require identification of activation times from electrogram recordings which themselves may have complex morphology or low signal-to-noise ratio. This paper surveys algorithms designed for identifying local activation times and computing conduction direction and speed. Their suitability for use in different recording contexts and applications is assessed.

A system and method for online high-resolution mapping of gastric slow-wave activity

High-resolution (HR) mapping employs multielectrode arrays to achieve spatially detailed analyses of propagating bioelectrical events. A major current limitation is that spatial analyses must currently be performed "off-line" (after experiments), compromising timely recording feedback and restricting experimental interventions. These problems motivated development of a system and method for "online" HR mapping. HR gastric recordings were acquired and streamed to a novel software client. Algorithms were devised to filter data, identify slow-wave events, eliminate corrupt channels, and cluster activation events. A graphical user interface animated data and plotted electrograms and maps. Results were compared against off-line methods. The online system analyzed 256-channel serosal recordings with no unexpected system terminations with a mean delay 18 s. Activation time marking sensitivity was 0.92; positive predictive value was 0.93. Abnormal slow-wave patterns including conduction blocks, ectopic pacemaking, and colliding wave fronts were reliably identified. Compared to traditional analysis methods, online mapping had comparable results with equivalent coverage of 90% of electrodes, average RMS errors of less than 1 s, and CC of activation maps of 0.99. Accurate slow-wave mapping was achieved in near real-time, enabling monitoring of recording quality and experimental interventions targeted to dysrhythmic onset. This work also advances the translation of HR mapping toward real-time clinical application.

The gastrointestinal electrical mapping suite (GEMS): software for analyzing and visualizing high-resolution (multi-electrode) recordings in spatiotemporal detail

Background

Gastrointestinal contractions are controlled by an underlying bioelectrical activity. High-resolution spatiotemporal electrical mapping has become an important advance for investigating gastrointestinal electrical behaviors in health and motility disorders. However, research progress has been constrained by the low efficiency of the data analysis tasks. This work introduces a new efficient software package: GEMS (Gastrointestinal Electrical Mapping Suite), for analyzing and visualizing high-resolution multi-electrode gastrointestinal mapping data in spatiotemporal detail.

Results

GEMS incorporates a number of new and previously validated automated analytical and visualization methods into a coherent framework coupled to an intuitive and user-friendly graphical user interface. GEMS is implemented using MATLAB®, which combines sophisticated mathematical operations and GUI compatibility. Recorded slow wave data can be filtered via a range of inbuilt techniques, efficiently analyzed via automated event-detection and cycle clustering algorithms, and high quality isochronal activation maps, velocity field maps, amplitude maps, frequency (time interval) maps and data animations can be rapidly generated. Normal and dysrhythmic activities can be analyzed, including initiation and conduction abnormalities. The software is distributed free to academics via a community user website and forum (http://sites.google.com/site/gimappingsuite).

Conclusions

This software allows for the rapid analysis and generation of critical results from gastrointestinal high-resolution electrical mapping data, including quantitative analysis and graphical outputs for qualitative analysis. The software is designed to be used by non-experts in data and signal processing, and is intended to be used by clinical researchers as well as physiologists and bioengineers. The use and distribution of this software package will greatly accelerate efforts to improve the understanding of the causes and clinical consequences of gastrointestinal electrical disorders, through high-resolution electrical mapping.

Automated gastric slow wave cycle partitioning and visualization for high-resolution activation time maps

High-resolution (HR) multi-electrode mapping has become an important technique for evaluating gastrointestinal (GI) slow wave (SW) behaviors. However, the application and uptake of HR mapping has been constrained by the complex and laborious task of analyzing the large volumes of retrieved data. Recently, a rapid and reliable method for automatically identifying activation times (ATs) of SWs was presented, offering substantial efficiency gains. To extend the automated data-processing pipeline, novel automated methods are needed for partitioning identified ATs into their propagation cycles, and for visualizing the HR spatiotemporal maps. A novel cycle partitioning algorithm (termed REGROUPS) is presented. REGROUPS employs an iterative REgion GROwing procedure and incorporates a Polynomial-surface-estimate Stabilization step, after initiation by an automated seed selection process. Automated activation map visualization was achieved via an isochronal contour mapping algorithm, augmented by a heuristic 2-step scheme. All automated methods were collectively validated in a series of experimental test cases of normal and abnormal SW propagation, including instances of patchy data quality. The automated pipeline performance was highly comparable to manual analysis, and outperformed a previously proposed partitioning approach. These methods will substantially improve the efficiency of GI HR mapping research.

Imaging ventricular fibrillation

Ventricular fibrillation (VF) had been traditionally considered as a highly disorganized process of random electrical activity emanating from multiple, short-lived, reentrant electrical waves. It is the incessant breakup of wave fronts and the creation of new daughter waves (wavebreaks) that perpetuate VF. Other studies described VF as a process with a substantial degree of structure embedded in seemingly random events where VF is spatially organized as a small number of relatively large domains, each with a single dominant frequency. Ventricular fibrillation is then driven by the domain with the highest activation frequency representing a "mother rotor" that drives the surrounding myocardium except at boundaries with more refractory tissues. Voltage-sensitive dyes and optical mapping provide a powerful technique that has been extensively applied to study the structure and organization of VF and has revealed how cellular properties, fiber orientation, and metabolism influence VF. This brief review will discuss signal processing methods used to investigate mechanisms underlying VF, namely, (a) fast Fourier transform, (b) time-frequency domain analysis, (c) time-lag correlation, (d) mutual information analysis, and (e) phase reconstruction techniques to identify phase singularities and wavebreak locations. In addition, several cellular properties that have been shown to influence the structure of VF such as (a) the dispersion of repolarization, (b) the low tonicity/osmolarity, and (c) the amplitude of K(+) currents will be discussed as determinants of VF. Finally, recent image analysis routines were used to identify wavebreak sites and revealed that wavebreaks are caused by abrupt spatial dispersion of voltage (V(m)) oscillations.

Multichannel data acquisition system for mapping the electrical activity of the heart

BACKGROUND

Details of the electrical conduction pattern of the heart are revealed to the electrophysiologist when multichannel data are used for activation mapping. Commercial electronic systems are available for simultaneous acquisition of many surface electrograms; however, the cost of these systems may be prohibitive and they can be mostly inflexible for adaptation to other research projects. Furthermore, the hardware and software design is often proprietary. In this article we describe the in-house design and implementation of a 320-multichannel acquisition system for animal electrophysiologic research.

METHOD AND RESULTS

Several modules comprise this system. The multichannel data are first preprocessed by amplification, filtering, and analog multiplexing. An algorithm for automatic adjustment of signal gains is implemented to maximize the voltage resolution and minimize noise pickup. Signals are then digitized, and sequenced to order the multichannel data and to add markers required for analysis. The digital data are streamed to archival storage media. Additionally, the electrocardiogram (ECG), blood pressure, and stimulus channel signals are stored simultaneously. Selected signals are then displayed in real-time for measurement and analysis and as a check of the system integrity. Examples of multielectrode arrays and surface recordings are provided. Costs for building such a system are estimated.

CONCLUSIONS

Multichannel data acquisition systems that are designed and constructed in-house have several advantages over turnkey commercial systems, including the potential for considerable cost savings, flexibility in acquiring data, and the ability to subsequently add additional components.

Fiber orientation and cell-cell coupling influence ventricular fibrillation dynamics

Cell Coupling Influences VF Dynamics.

INTRODUCTION

The structure of ventricular fibrillation (VF) is influenced by regional differences in action potential durations and perhaps restitution kinetics and fiber anisotropy. The spatial organization of VF was investigated by measuring the cross-correlation (CC) and mutual information (MI) of membrane potential (Vm) oscillations recorded from multiple sites.

METHODS AND RESULTS

Rabbit hearts (n = 6) were retrogradely perfused and stained with di-4-ANEPPS, and VF was elicited by burst pacing. Vm oscillations were recorded optically from multiple locations on the epicardium using a 16 x 16 photodiode array or a 72 x 78 CCD camera. The spatial organization of VF was investigated by calculating the maximum CC (CCmax) and MI (MImax) that can be obtained between any two sites. CCmax and MImax were extended to all pixels and served as indices of the similarities between Vm transients at a reference pixel and all other pixels on the map. We found that maps of CCmax and MImax did not contain discrete regions with high CC or MI. However, CCmax and MImax decreased monotonically with increasing distance between any arbitrarily chosen reference pixel and all other pixels. In VF, maps of CCmax and MImax revealed elliptical gradients of CC and MI that were closely aligned with fiber orientation, with major axis at 127 degrees +/- 8 degrees on the left ventricles.

CONCLUSION

CC and MI analysis in fibrillation provides new evidence that anisotropy of fiber orientation and cell-cell coupling have a direct influence on VF dynamics.

Dynamics and interaction of filaments in a computational model of re-entrant ventricular fibrillation

Ventricular fibrillation (VF) is a lethal cardiac arrhythmia. Re-entry, in which action potential wavefronts rotate around filaments, is believed to sustain VF. In this study we used a computational model of multiple wavelet fibrillation in the thin-walled right ventricle (10 mm thick) and the thicker walled left ventricle (16 mm thick) to investigate the effect of tissue thickness and initiation protocol on re-entry, and to examine whether filament dynamics and interaction in the model could explain why re-entry is both rarely observed and short-lived in experimental studies that map electrical activation on the heart surface. We found (i) that the density of filaments, the proportion of transmural filaments and the proportion of filaments visible on the model surface were all higher in the 10 mm simulation, (ii) that the initiation protocol influences the rate of filament breakdown but not the number of filaments present after 1 s, and (iii) that although many filaments are visible on the surface of the model, the majority are visible for less than one rotation. This study shows that tissue thickness, geometry and initiation protocol influence electrical activation during VF, and that the rapid motion and interaction of filaments result in transient appearance of surface re-entry.

Characterization of patterned irregularity in locally interacting, spatially extended systems: Ventricular fibrillation

The re-entrant ventricular arrhythmias of monomorphic ventricular tachycardia and fibrillation are produced by abnormal spatio-temporal patterns of propagation in the ventricular myocardium. These behaviors can be described by solutions of reaction-diffusion equation excitable medium models. The direct comparison of such solutions with existing experimental observations is virtually impossible as there are too many factors to be taken into account, including not only the complicated dynamics of the re-entrant waves of excitation in the tissue, but also the way the appearance of these waves on the surface is modified by the inhomogeneity, anisotropy and three-dimensional nature of heart tissue. One way of indirect comparison is to compare characteristics of the complexity of the model and the real data, that are invariant under these modifications of the signal. Karhunen-Loeve decomposition is a standard tool for evaluating the complexity of multidimensional signals. A comparison of the separate and conjoint complexities of the signals on the opposite sides of the preparation can be considered as an indicator how much three-dimensional effects are essential in the preparation behavior. (c) 2001 American Institute of Physics.

Spatial correlation analysis of atrial activation patterns during sustained atrial fibrillation in conscious goats

In this study we applied both linear and nonlinear spatial correlation measures to characterize epicardial activation patterns of sustained atrial fibrillation in instrumented conscious goats. It was investigated if nonlinearity was involved in the spatial coupling of atrial regions and to what extent fibrillation was organized in the experimental model of sustained atrial fibrillation (AF) in instrumented goats. Data were collected in five goats during experiments to convert AF by continuous infusion of cibenzoline. Spatial organization during AF was quantified with the linear spatial cross correlation function and the nonlinear spatial cross redundancy which was calculated using the Grassberger-Procaccia correlation integral. Two different types of correlation were evaluated to distinguish simultaneous interaction from non-simultaneous interaction, for instance resulting from propagation of fibrillation waves. The nonlinear association length and the linear correlation length were estimated along the principal axes of iso-correlation contours in two-dimensional correlation maps of the nonlinear spatial redundancy and the linear spatial correlation function, respectively. To quantitatively assess the degree of nonlinearity, the association length was also estimated from the linearized spatial redundancy using multivariate surrogate data. The differences between the nonlinear and linearized association lengths indicated that a nonlinear component in the spatial organization of AF predominantly existed in the right atrium. The degree of organization characterized by association length along the short principal axis was higher in the right atrium (15 +/- 7 mm) than in the left atrium (8 +/- 4 mm). The spatial extension of coherent atrial patches was estimated from a surface of association equal to the area spanned by the principal axes of iso-correlation contours from the redundancy, including the effects from non-simultaneous interaction. Interpreting this area as the spatial domain of a fibrillation wavelet, the results suggest that the mapped region was activated on average by two wavelets in the left atrium and by one wavelet in the right atrium. Therefore, the activation pattern of sustained AF in goats was relatively organized, consistent with type II of AF. It is suggested that the surface of association is a measure of the number of independent wavelets present in the atria during sustained AF, and that larger association lengths result from fewer and larger reentrant circuits.

Using an artificial neural network to detect activations during ventricular fibrillation

Ventricular fibrillation is a cardiac arrhythmia that can result in sudden death. Understanding and treatment of this disorder would be improved if patterns of electrical activation could be accurately identified and studied during fibrillation. A feedforward artificial neural network using backpropagation was trained with the Rule-Based Method and the Current Source Density Method to identify cardiac tissue activation during fibrillation. Another feedforward artificial neural network that used backpropagation was trained with data preprocessed by those methods and the Transmembrane Current Method. Staged training, a new method that uses different sets of training examples in different stages, was used to improve the ability of the artificial neural networks to detect activation. Both artificial neural networks were able to correctly classify more than 92% of new test examples. The performance of both artificial neural networks improved when staged training was used. Thus, artificial neural networks may beuseful for identifying activation during ventricular fibrillation.

Cardiac muscle tissue engineering: toward an in vitro model for electrophysiological studies

The objective of this study was to establish a three-dimensional (3-D) in vitro model system of cardiac muscle for electrophysiological studies. Primary neonatal rat ventricular cells containing lower or higher fractions of cardiac myocytes were cultured on polymeric scaffolds in bioreactors to form regular or enriched cardiac muscle constructs, respectively. After 1 wk, all constructs contained a peripheral tissue-like region (50-70 micrometer thick) in which differentiated cardiac myocytes were organized in multiple layers in a 3-D configuration. Indexes of cell size (protein/DNA) and metabolic activity (tetrazolium conversion/DNA) were similar for constructs and neonatal rat ventricles. Electrophysiological studies conducted using a linear array of extracellular electrodes showed that the peripheral region of constructs exhibited relatively homogeneous electrical properties and sustained macroscopically continuous impulse propagation on a centimeter-size scale. Electrophysiological properties of enriched constructs were superior to those of regular constructs but inferior to those of native ventricles. These results demonstrate that 3-D cardiac muscle constructs can be engineered with cardiac-specific structural and electrophysiological properties and used for in vitro impulse propagation studies.