Twelve lead electrocardiogram signals capture unique fingerprints about the body's
biological processes and electrical activity of heart muscles. Machine learning and deep …

The risk of arrhythmia and its management become increasingly complex as kidney disease
progresses. This presents a multifaceted clinical challenge. Our discussion addresses these …

Objective Predicting mortality after acute myocardial infarction (AMI) is crucial for timely
prescription and treatment of AMI patients, but there are no appropriate AI systems for …

Cardiac arrhythmia is one of the prime reasons for death globally. Early diagnosis of heart
arrhythmia is crucial to provide timely medical treatment. Heart arrhythmias are diagnosed …

The global burden of atrial fibrillation (AFIB) is constantly increasing, and its early detection
is still a challenge for public health and motivates researchers to improve methods for …

Electrocardiogram (ECG) is a non-invasive approach to capture the overall electrical activity
produced by the contraction and relaxation of the cardiac muscles. It has been established …

Ventricular fibrillation (VF) is a critical ventricular arrhythmia with severe consequences. Due
to the severity of VF, it urgently requires a rapid and accurate detection of abnormal patterns …

Identifying and accurately classifying cardiac abnormalities, including myocardial infarction
(MI) and cardiac arrhythmia (CA), remains a significant challenge in the field of cardiology …

In recent years, a wide variety of Machine Learning (ML) algorithms, including Deep
Learning (DL) methods, have been proposed for electrocardiogram (ECG) beat …

Visualizing the decision-making process is a key aspect of research regarding explainable
arrhythmia recognition. This study proposed a visualized lead selection method to classify …