The prevalence of cardiovascular diseases is increasing around the world. However, the
technology is evolving and can be monitored with low-cost sensors anywhere at any time …

Contemporary methods used to interpret the electrocardiogram (ECG) signal for diagnosis
or monitoring are based on expert knowledge and rule-centered algorithms. In recent years …

Atrial fibrillation (AF) affects more than 30 million individuals worldwide, making it the most
prevalent cardiac arrhythmia on a global scale. This systematic review summarizes recent …

Postoperative atrial fibrillation (POAF) occurs in up to 20% to 55% of patients who
underwent cardiac surgery. Machine learning (ML) has been increasingly employed in …

Leveraging artificial intelligence (AI) for the analysis of electrocardiograms (ECG) has the
potential to transform diagnosis and estimate the prognosis of not only cardiac but …

Background Artificial intelligence (AI) has emerged as a tool to potentially increase the
efficiency and efficacy of cardiovascular care and improve clinical outcomes. This study aims …

This article presents an overview of existing approaches to perform vectorcardiographic
(VCG) diagnostics of ischemic heart disease (IHD). Individual methodologies are divided …

Atrial fibrillation (AF) in the postoperative phase is a manifestation of numerous factors,
including surgical stress, anesthetic effects, and underlying cardiovascular conditions. The …

Atrial fibrillation (AF) is the most prevalent form of arrhythmia, with a rising incidence and
prevalence worldwide, posing significant implications for public health. In this paper, we …

Postoperative atrial fibrillation (POAF) after cardiac surgery is associated with elevated
morbidity and mortality. Although current prediction models have limited efficacy, several …