Enhancement of central norepinephrinergic neurotransmission contributes to inhibition of seizure-induced respiratory arrest by targeting the beta-1 adrenergic receptor (β1-AR) locating in the cardiomyocytes in the DBA/1 mouse SUDEP model

Sudden unexpected death of epilepsy (SUDEP) is the key cause of of death in patients with epilepsy. Due to the complicated pathogenesis of SUDEP, however, the exact mechanism of SUDEP remains elusive. Currently, although it is recognized that the seizure-induced respiratory arrest (S-IRA) may be a main cause for SUDEP, other factors resulting in SUDEP can not be excluded e.g arrhythmias. Our previous findings indicated that the incidence of seizure-induced respiratory arrest S-IRA and SUDEP evoked by acoustic stimulation or pentetrazol (PTZ) injection was significantly reduced by atomoxetine, a norepinephrine reuptake inhibitor (NRI), suggesting that noradrenergic neurotransmission modulates S-IRA and SUDEP. Given that norepinephrine acts on the central and peripheral target to modulate respiratory and circulation function by targeting adrenergic receptor α and beta (a-AR and β-AR) and the arrhythmias can be contributed to SUDEP. Meanwhile, to further test whether cardiac factors are implicated in S-IRA and SUDEP, we choose esmolol hydrochloride, a selective antagonist of beta-1 adrenergic receptor (β1-AR) to test it in our models. Our findings demonstrated that the lower incidence of S-IRA and SUDEP evoked by acoustic stimulation or PTZ in DBA/1 mice by administration with atomoxetine was significantly reversed by intraperitoneal (IP) of esmolol hydrochloride. Importantly, the data of electrocardiogram (ECG) showed that the cardiac arrhythmia evoked by acoustic stimulation including the ventricular tachycardia, ventricular premature beat and atrioventricular block and administration of atomoxetine significantly reduced theses arrhythmias and the incidence of S-IRA and SUDEP in our models. Thus, the dysfunction of respiratory and circulation may be implicated in the pathogenesis of S-IRA and SUDEP hand in hand and enhancing central norepinephrinergic neurotransmission contributes to inhibition of seizure-induced respiratory arrest by targeting β1-AR locating in the cardiomyocytes. Our findings will show a new light on decoding the pathogenesis of SUDEP. Keywords: sudden unexpected death in epilepsy (SUDEP); seizure-induced respiratory arrest S-IRA); esmolol hydrochloride (Esmolol); Electrocardiogram (ECG); locus coeruleus (LC); cardiac arrhythmia; pentetrazol (PTZ)

Modeling Human Cardiac Arrhythmias: Insights from Zebrafish

Cardiac arrhythmia, or irregular heart rhythm, is associated with morbidity and mortality and is described as one of the most important future public health challenges. Therefore, developing new models of cardiac arrhythmia is critical for understanding disease mechanisms, determining genetic underpinnings, and developing new therapeutic strategies. In the last few decades, the zebrafish has emerged as an attractive model to reproduce in vivo human cardiac pathologies, including arrhythmias. Here, we highlight the contribution of zebrafish to the field and discuss the available cardiac arrhythmia models. Further, we outline techniques to assess potential heart rhythm defects in larval and adult zebrafish. As genetic tools in zebrafish continue to bloom, this model will be crucial for functional genomics studies and to develop personalized anti-arrhythmic therapies.

ConvNet: 1D-Convolutional Neural Networks for Cardiac Arrhythmia Recognition Using ECG Signals

In healthcare, diagnostic tools of cardiac diseases are commonly known by the electrocardiogram (ECG) analysis. Atypical electrical activity can produce a cardiac arrhythmia. Various difficulties can be imposed to clinicians e.g., myocardial infarction arrhythmia via the non-stationarity and irregularity heart beat signals. Through the assistance of computer-aided diagnosis methods, timely specification of arrhythmia diseases reduces the mortality rate of affected patients. In this study, a 1 Lead QRS complex -layer deep convolutional neural network is proposed for the recognition of arrhythmia datasets. By the use of this CNN model, we planned a complete structure of the classification architecture after a pre-processing stage counting the denoising and QRS complex signals detection procedure. The chief benefit of the new proposed methodology is that the automatically training the QRS complexes without requiring all original extracted ECG signals. The proposed model was trained on the increased ECG database and separated into five classes. Experimental results display that the established CNN method has improved performance when compared to the state-of-the-art studies.

Expert-enhanced machine learning for cardiac arrhythmia classification

We propose a new method for the classification task of distinguishing atrial fibrillation (AFib) from regular atrial tachycardias including atrial flutter (AFlu) based on a surface electrocardiogram (ECG). Recently, many approaches for an automatic classification of cardiac arrhythmia were proposed and to our knowledge none of them can distinguish between these two. We discuss reasons why deep learning may not yield satisfactory results for this task. We generate new and clinically interpretable features using mathematical optimization for subsequent use within a machine learning (ML) model. These features are generated from the same input data by solving an additional regression problem with complicated combinatorial substructures. The resultant can be seen as a novel machine learning model that incorporates expert knowledge on the pathophysiology of atrial flutter. Our approach achieves an unprecedented accuracy of 82.84% and an area under the receiver operating characteristic (ROC) curve of 0.9, which classifies as “excellent” according to the classification indicator of diagnostic tests. One additional advantage of our approach is the inherent interpretability of the classification results. Our features give insight into a possibly occurring multilevel atrioventricular blocking mechanism, which may improve treatment decisions beyond the classification itself. Our research ideally complements existing textbook cardiac arrhythmia classification methods, which cannot provide a classification for the important case of AFib↔AFlu. The main contribution is the successful use of a novel mathematical model for multilevel atrioventricular block and optimization-driven inverse simulation to enhance machine learning for classification of the arguably most difficult cases in cardiac arrhythmia. A tailored Branch-and-Bound algorithm was implemented for the domain knowledge part, while standard algorithms such as Adam could be used for training.

Impact of racial differences on the epidemiology and clinical outcomes of atrial fibrillation

: Atrial fibrillation (AF) is the most common cardiac arrhythmia and is associated with increased morbidity and mortality. The overall AF burden is expected to rise over the next decade, and this will have significant implications on the healthcare cost. Current literature on the pathophysiology, epidemiology and management of patients with AF have focused mainly on predominantly Caucasian/white population while published studies in non-white populations have been mainly observational or retrospective in nature. Hence, the implications of AF in terms of management and complications in the non-white population have not been fully appreciated. In this review, we summarized based on the available literature, the racial differences in the prevalence, management and outcomes of patients with AF comparing the white vs non-white population.