Atrial Fibrillation (AF) is the most common cardiac arrhythmia. Signal-processing approaches are widely used for the analysis of intracardiac electrograms (iEGMs), which are collected during catheter ablation from patients with AF. In order to identify possible targets for ablation therapy, dominant frequency (DF) is widely used and incorporated in electroanatomical mapping systems. Recently, a more robust measure, multiscale frequency (MSF), for iEGM data analysis was adopted and validated. However, before completing any iEGM analysis, a suitable bandpass (BP) filter must be applied to remove noise. Currently, no clear guidelines for BP filter characteristics exist. The lower bound of the BP filter is usually set to 3–5 Hz, while the upper bound (${\overline{BP}}^{th}$) of the BP filter varies from 15 Hz to 50 Hz according to many researchers. This large range of ${\overline{BP}}^{th}$ subsequently affects the efficiency of further analysis. In this paper, we aimed to develop a data-driven preprocessing framework for iEGM analysis, and validate it based on DF and MSF techniques. To achieve this goal, we optimized the ${\overline{BP}}^{th}$ using a data-driven approach (DBSCAN clustering) and demonstrated the effects of different ${\overline{BP}}^{th}$ on subsequent DF and MSF analysis of clinically recorded iEGMs from patients with AF. Our results demonstrated that our preprocessing framework with ${\overline{BP}}^{th}$ = 15 Hz has the best performance in terms of the highest Dunn index. We further demonstrated that the removal of noisy and contact-loss leads is necessary for performing correct data iEGMs data analysis.

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 ( $\tau$ ), as well as MSE and Kt values can be successfully used to discriminate between Pers. AF and NSR. Moreover, we demonstrate that $\tau$ 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.

Bionic-engineered tissues have been proposed for testing the performance of cardiovascular medical devices and predicting clinical outcomes ex vivo. Progress has been made in the development of compliant electronics that are capable of monitoring treatment parameters and being coupled to engineered tissues; however, the scale of most engineered tissues is too small to accommodate the size of clinical-grade medical devices. Here, we show substantial progress toward bionic tissues for evaluating cardiac ablation tools by generating a centimeter-scale human cardiac disk and coupling it to a hydrogel-based soft-pressure sensor. The cardiac tissue with contiguous electromechanical function was made possible by our recently established method to 3D bioprint human pluripotent stem cells in an extracellular matrix-based bioink that allows for in situ cell expansion prior to cardiac differentiation. The pressure sensor described here utilized electrical impedance tomography to enable the real-time spatiotemporal mapping of pressure distribution. A cryoablation tip catheter was applied to the composite bionic tissues with varied pressure. We found a close correlation between the cell response to ablation and the applied pressure. Under some conditions, cardiomyocytes could survive in the ablated region with more rounded morphology compared to the unablated controls, and connectivity was disrupted. This is the first known functional characterization of living human cardiomyocytes following an ablation procedure that suggests several mechanisms by which arrhythmia might redevelop following an ablation. Thus, bionic-engineered testbeds of this type can be indicators of tissue health and function and provide unique insight into human cell responses to ablative interventions.