Deep Learning (DL), a successful promising approach for discriminative and generative
tasks, has recently proved its high potential in 2D medical imaging analysis; however …

In the health care and medical domain, it has been proven challenging to diagnose correctly
many diseases with complicated and interferential symptoms, including arrhythmia …

Early diagnosis and classification of arrhythmia from an electrocardiogram (ECG) plays a
significant role in smart healthcare systems for the health monitoring of individuals with …

ECG is a non-invasive tool used to detect cardiac arrhythmias. Many arrhythmias
classification solutions with various ECG features have been reported in literature. In this …

This paper presents ArrhyMon, a self-attention-based LSTM-FCN model for arrhythmia
classification from ECG signal inputs. ArrhyMon targets to detect and classify six different …

Premature ventricular contraction (PVC) is one of the most common arrhythmias which can
cause palpitation, cardiac arrest, and other symptoms affecting the work and rest activities of …

The 12-lead electrocardiogram (ECG) is widely used in the clinical diagnosis of
cardiovascular disease, and deep learning has become an effective approach to automatic …

An electrocardiogram (ECG) is an electrical signal produced by ECG sensors to examine
and visualize the heart's functionality, quick identification of arrhythmia aids in proper care …

Objective. Learning to classify cardiac abnormalities requires large and high-quality labeled
datasets, which is a challenge in medical applications. Small datasets from various sources …

Artificial intelligence (AI) aims to mimic human cognitive functions and execute intellectual
activities like that performed by humans dealing with an uncertain environment. The rapid …