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 …

The automated analysis of electrocardiogram (ECG) signals plays a crucial role in the early
diagnosis and management of cardiac arrhythmias. The diverse etiology of arrhythmia and …

In this work, we proposed a hybrid deep learning model that (CNN-GRU) combines a One-
Dimensional Neural Network (1D CNN) and a Gated Recurrent Unit (GRU) to classify four …

An automatic system for heart arrhythmia classification can perform a substantial role in
managing and treating cardiovascular diseases. In this paper, a deep learning-based multi …

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 …

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 …

With the development of deep learning-based methods, automated classification of
electrocardiograms (ECGs) has recently gained much attention. Although the effectiveness …

Managing and treating cardiovascular diseases can be substantially improved by automatic
detection and classification of the heart arrhythmia. In this paper, we introduced a novel …

In recent years, with the development of artificial intelligence, deep learning model has
achieved initial success in ECG data analysis, especially the detection of atrial fibrillation. In …

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 …