Electrocardiogram (ECG) signal analysis can be time-consuming, tedious, and error-prone.
Therefore, automated analysis is need of time that will assist clinicians in detecting cardiac …

Background Early detection and intervention is the cornerstone for appropriate treatment of
arrhythmia and prevention of complications and mortality. Although diverse deep learning …

Cardiovascular diseases are a leading cause of mortality globally. Electrocardiography
(ECG) still represents the benchmark approach for identifying cardiac irregularities …

Cardiac arrhythmia is a prevalent and significant cause of morbidity and mortality among
cardiac ailments. Early diagnosis is crucial in providing intervention for patients suffering …

Deep learning (DL) has been introduced in automatic heart-abnormality classification using
ECG signals, while its application in practical medical procedures is limited. A systematic …

Arrhythmia is a disorder of the heart caused by the erratic nature of heartbeats occurring due
to conduction failures of the electrical signals in the cardiac muscle. In recent years …

An electrocardiograph (ECG) is employed as a primary tool for diagnosing cardiovascular
diseases (CVDs). ECG signals provide a framework to probe the underlying properties and …

Cardiac arrhythmias are presently diagnosed by manual interpretation of
Electrocardiography (ECG) signals. Automated ECG interpretation is required to perform …

Objective: Electrocardiogram (ECG) is the most frequent and routine diagnostic tool used for
monitoring heart electrical signals and evaluating its functionality. The human heart can …

Deep Learning-based methods have emerged to be one of the most effective and practical
solutions in a wide range of medical problems, including the diagnosis of cardiac …