Search for: All records

Transparent microelectrode arrays (MEAs) that allow multimodal investigation of the spatiotemporal cardiac characteristics are important in studying and treating heart disease. Existing implantable devices, however, are designed to support chronic operational lifetimes and require surgical extraction when they malfunction or are no longer needed. Meanwhile, bioresorbable systems that can self-eliminate after performing temporary functions are increasingly attractive because they avoid the costs/risks of surgical extraction. We report the design, fabrication, characterization, and validation of a soft, fully bioresorbable, and transparent MEA platform for bidirectional cardiac interfacing over a clinically relevant period. The MEA provides multiparametric electrical/optical mapping of cardiac dynamics and on-demand site-specific pacing to investigate and treat cardiac dysfunctions in rat and human heart models. The bioresorption dynamics and biocompatibility are investigated. The device designs serve as the basis for bioresorbable cardiac technologies for potential postsurgical monitoring and treating temporary patient pathological conditions in certain clinical scenarios, such as myocardial infarction, ischemia, and transcatheter aortic valve replacement. 

The goal of motion tomography is to recover a description of a vector flow field using measurements along the trajectory of a sensing unit. In this paper, we develop a predictor corrector algorithm designed to recover vector flow fields from trajectory data with the use of occupation kernels developed by Rosenfeld et al. [9,10]. Specifically, we use the occupation kernels as an adaptive basis; that is, the trajectories defining our occupation kernels are iteratively updated to improve the estimation in the next stage. Initial estimates are established, then under mild assumptions, such as relatively straight trajectories, convergence is proven using the Contraction Mapping Theorem. We then compare the developed method with the established method by Chang et al. [5] by defining a set of error metrics. We found that for simulated data, where a ground truth is available, our method offers a marked improvement over [5]. For a real-world example, where ground truth is not available, our results are similar results to the established method.