Electrocardiographic imaging (ECGI) has recently gained attention as a viable diagnostic tool for reconstructing cardiac electrical activity in normal hearts as well as in cardiac arrhythmias. However, progress has been limited by the lack of both standards and unbiased comparisons of approaches and techniques across the community, as well as the consequent difficulty of effective collaboration across research groups.. To address these limitations, we created the Consortium for Electrocardiographic Imaging (CEI), with the objective of facilitating collaboration across the research community in ECGI and creating standards for comparisons and reproducibility. Here we introduce CEI and describe its two main efforts, the creation of EDGAR, a public data repository, and the organization of three collaborative workgroups that address key components and applications in ECGI. Both EDGAR and the workgroups will facilitate the sharing of ideas, data and methods across the ECGI community and thus address the current lack of reproducibility, broad collaboration, and unbiased comparisons.
INTRODUCTION: The "Experimental Data and Geometric Analysis Repository", or EDGAR is an Internet-based archive of curated data that are freely distributed to the international research community for the application and validation of electrocardiographic imaging (ECGI) techniques. The EDGAR project is a collaborative effort by the Consortium for ECG Imaging (CEI, ecg-imaging.org), and focused on two specific aims. One aim is to host an online repository that provides access to a wide spectrum of data, and the second aim is to provide a standard information format for the exchange of these diverse datasets. METHODS: The EDGAR system is composed of two interrelated components: 1) a metadata model, which includes a set of descriptive parameters and information, time signals from both the cardiac source and body-surface, and extensive geometric information, including images, geometric models, and measure locations used during the data acquisition/generation; and 2) a web interface. This web interface provides efficient, search, browsing, and retrieval of data from the repository. RESULTS: An aggregation of experimental, clinical and simulation data from various centers is being made available through the EDGAR project including experimental data from animal studies provided by the University of Utah (USA), clinical data from multiple human subjects provided by the Charles University Hospital (Czech Republic), and computer simulation data provided by the Karlsruhe Institute of Technology (Germany). CONCLUSIONS: It is our hope that EDGAR will serve as a communal forum for sharing and distribution of cardiac electrophysiology data and geometric models for use in ECGI research.
Cardiac electrical imaging, that is, reconstructing car- diac electrical activity from body surface measurements, is a technology with great potential. However, ill-posedness of this problem hinders its routine usage in clinical envi- ronment and continues to motivate the search for improve- ments on current methods. Messnarz et al. introduced an algorithm that constraints the reconstructed transmem- brane potential (TMP) to be non-decreasing over time dur- ing QRS-complex. This physiologically meaningful con- straint reduces the solution space of the problem and reg- ularizes the solution. However, this approach is compu- tationally extensive and can become prohibitive as spatial and temporal resolution of the problem increase. Here we compare three distinct options to reduce the computational load: downsampling the measurements in time, downsam- pling the measurements after filtering with an algorithm based on principal component analysis and non-linearly interpolating the potentials with a spline-based method. The data used were simulated TMPs that were forward propagated to the body surface in a densely sampled ge- ometry. The resulting body surface potential simulations were corrupted with noise and the inverse computed using a much coarser mesh to take geometry errors into account. The results indicate that reducing the dimension of the sig- nal in time does not reduce the quality of the solutions obtained, while the computational requirements decrease considerably, especially for the spline method.