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Cardiac myocytes synchronize through electrical signaling to contract heart muscles, facilitated by gap junctions (GJs) located in the intercalated disc (ID). GJs provide low-resistance pathways for electrical impulse propagation between myocytes, considered the primary mechanism for electrical communication in the heart. However, research indicates that conduction can persist without GJs. Ephaptic coupling (EpC), which depends on electrical fields in the narrow ID between adjacent myocytes, offers an alternative mechanism for cardiac conduction when GJs are impaired. Research suggests that EpC can enhance conduction velocity (CV) and reduce the likelihood of conduction block (CB), particularly when GJs are impaired, demonstrating the anti-arrhythmic potential of EpC. Reduced GJ communication increases the susceptibility of heart to arrhythmias due to ectopic or triggered activity, highlighting the pro-arrhythmic effect of GJ uncoupling. However, the interplay between GJs and EpC, and their roles in the initiation, dynamics, and termination of arrhythmias, remain unclear. Reentry, characterized by a loop of electrical activity, is a common mechanism underlying arrhythmogenesis in the heart. This study aims to explore the interplay between EpC and GJs on reentry initiation and its underlying dynamics. Specifically, we employed a two-dimensional (2D) discrete bidomain model that integrates EpC to simulate ephaptic conduction during reentry. We quantitatively assessed the outcomes of reentry initiation and the resulting dynamics across different levels of EpC, GJs, and initial perturbations. The results show that sufficiently strong EpC (i.e., sufficiently narrow clefts) tends to suppress reentry initiation, resulting in absent or non-sustained reentrant activity, while also introducing transient instability and heterogeneity into the cardiac dynamics. In contrast, relatively weak EpC (wide clefts) support sustained reentry with a stable rotor. Furthermore, we found that sufficiently strong EpC can lower the maximal dominant frequency observed during reentrant activity. Overall, this suggests that strong EpC exerts an anti-arrhythmic effect. 

Objective: Rotors, regions of spiral wave reentry in cardiac tissues, are considered as the drivers of atrial fibrillation (AF), the most common arrhythmia. Whereas physics-based approaches have been widely deployed to detect the rotors, in-depth knowledge in cardiac physiology and electrogram interpretation skills are typically needed. The recent leap forward in smart sensing, data acquisition, and Artificial Intelligence (AI) has offered an unprecedented opportunity to transform diagnosis and treatment in cardiac ailment, including AF. This study aims to develop an image-decomposition-enhanced deep learning framework for automatic identification of rotor cores on both simulation and optical mapping data. Methods: We adopt the Ensemble Empirical Mode Decomposition algorithm (EEMD) to decompose the original image, and the most representative component is then fed into a You-Only-Look-Once (YOLO) object-detection architecture for rotor detection. Simulation data from a bi-domain simulation model and optical mapping acquired from isolated rabbit hearts are used for training and validation. Results: This integrated EEMD-YOLO model achieves high accuracy on both simulation and optical mapping data (precision: 97.2%, 96.8%, recall: 93.8%, 92.2%, and F1 score: 95.5%, 94.4%, respectively). Conclusion: The proposed EEMD-YOLO yields comparable accuracy in rotor detection with the gold standard in literature. 

Abstract Life‐threatening ventricular arrhythmias and sudden cardiac death are often preceded by cardiac alternans, a beat‐to‐beat oscillation in the T‐wave morphology or duration. However, given the spatiotemporal and structural complexity of the human heart, designing algorithms to effectively suppress alternans and prevent fatal rhythms is challenging. Recently, an antiarrhythmic constant diastolic interval pacing protocol was proposed and shown to be effective in suppressing alternans in 0‐, 1‐, and 2‐dimensional in silico studies as well as in ex vivo whole heart experiments. Herein, we provide a systematic review of the electrophysiological conditions and mechanisms that enable constant diastolic interval pacing to be an effective antiarrhythmic pacing strategy. We also demonstrate a successful translation of the constant diastolic interval pacing protocol into an ECG‐based real‐time control system capable of modulating beat‐to‐beat cardiac electrical activity and preventing alternans. Furthermore, we present evidence of the clinical utility of real‐time alternans suppression in reducing arrhythmia susceptibility in vivo. We provide a comprehensive overview of this promising pacing technique, which can potentially be translated into a clinically viable device that could radically improve the quality of life of patients experiencing abnormal cardiac rhythms. 

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.