Severe obstructive sleep apnea in Tunisian population with atrial fibrillation: Prevalence and predictive factors

Epicardial adipose tissue, obesity and the occurrence of atrial fibrillation: an overview of pathophysiology and treatment methods

INTRODUCTION

Obesity is a chronic disease, which has significant health consequences and is a staggering burden to health care systems. Obesity can have harmful effects on the cardiovascular system, including heart failure, hypertension, coronary heart disease, and atrial fibrillation (AF). One of the possible substrates might be epicardial adipose tissue (EAT), which can be the link between AF and obesity. EAT is a fat deposit located between the myocardium and the visceral pericardium. Numerous studies have demonstrated that EAT plays a pivotal role in this relationship regarding atrial fibrillation.

AREAS COVERED

This review will focus on the role of obesity and the occurrence of atrial fibrillation (AF) and examine the connection between these and epicardial adipose tissue (EAT). The first part of this review will explain the pathophysiology of EAT and its association with the occurrence of AF. Secondly, we will review bariatric and metabolic surgery and its effects on EAT and AF.

EXPERT COMMENTARY

In this review, the epidemiology, pathophysiology, and treatments methods of AF are explained. Secondly the effects on EAT were elucidated. Due to the complex pathophysiological link between EAT, AF, and obesity, it is still uncertain which treatment strategy is superior.

Computational Diagnostic Techniques for Electrocardiogram Signal Analysis

Cardiovascular diseases (CVDs), including asymptomatic myocardial ischemia, angina, myocardial infarction, and ischemic heart failure, are the leading cause of death globally. Early detection and treatment of CVDs significantly contribute to the prevention or delay of cardiovascular death. Electrocardiogram (ECG) records the electrical impulses generated by heart muscles, which reflect regular or irregular beating activity. Computer-aided techniques provide fast and accurate tools to identify CVDs using a patient's ECG signal, which have achieved great success in recent years. Latest computational diagnostic techniques based on ECG signals for estimating CVDs conditions are summarized here. The procedure of ECG signals analysis is discussed in several subsections, including data preprocessing, feature engineering, classification, and application. In particular, the End-to-End models integrate feature extraction and classification into learning algorithms, which not only greatly simplifies the process of data analysis, but also shows excellent accuracy and robustness. Portable devices enable users to monitor their cardiovascular status at any time, bringing new scenarios as well as challenges to the application of ECG algorithms. Computational diagnostic techniques for ECG signal analysis show great potential for helping health care professionals, and their application in daily life benefits both patients and sub-healthy people.