Artificial intelligence (AI) is increasingly used in electrocardiography (ECG) to assist in
diagnosis, stratification, and management. AI algorithms can help clinicians in the following …

Background Electrocardiogram (ECG) is one of the most common noninvasive diagnostic
tools that can provide useful information regarding a patient's health status. Deep learning …

Arrhythmia has been classified using a variety of methods. Because of the dynamic nature of
electrocardiogram (ECG) data, traditional handcrafted approaches are difficult to execute …

Arrhythmias are defined as irregularities in the heartbeat rhythm, which may infrequently
occur in a human's life. These arrhythmias may cause potentially fatal complications, which …

Considering the significant burden to patients and healthcare systems globally related to
atrial fibrillation (AF) complications, the early AF diagnosis is of crucial importance. In the …

The electrocardiogram (ECG) is a highly effective non-invasive tool for monitoring heart
activity and diagnosing cardiovascular diseases (CVDs). Automatic detection of arrhythmia …

Arrhythmias are anomalies in the heartbeat rhythm that occur occasionally in people's lives.
These arrhythmias can lead to potentially deadly consequences, putting your life in …

Minimizing the interruption of cardiopulmonary resuscitation (CPR) is an important
technique to improve the survival of out-of-hospital cardiac arrest (OHCA) patients. Recent …

This study investigates the use of atrioventricular (AV) synchronization as an important
diagnostic criterion for atrial fibrillation and flutter (AF) using one to twelve ECG leads. Heart …

Background Diagnosis of shockable rhythms leading to defibrillation remains integral to
improving out‐of‐hospital cardiac arrest outcomes. New machine learning techniques have …