Background Cardiac arrhythmia is a cardiovascular disorder characterized by disturbances
in the heartbeat caused by electrical conduction anomalies in cardiac muscle. Clinically …

Arrhythmia classification is the need of the hour as the world is reporting a higher death troll
as a cause of cardiac diseases. Most of the existing methods developed for arrhythmia …

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 …

Automated electrocardiogram (ECG) analysis cannot be employed in clinical practice due to
the accuracy of the present models. Deep Neural Networks (DNNs) are models made up of …

Classifying different types of arrhythmias based on ECG signal is an important research
topic in healthcare. Traditional methods focus on extracting varieties of features from ECG …

In this paper, an intellectual based electrocardiogram (ECG) signal classification approach
utilizing Deep Learning (DL) is being developed. ECG plays important role in diagnosing …

Electrocardiogram (ECG) signals play a vital role in diagnosing and monitoring patients
suffering from various cardiovascular diseases (CVDs). This research aims to develop a …

In the context of Cardiovascular Diseases, arrhythmia is one of the causes of sudden death,
which is related to abnormal electrical activities of the heart that can be reflected by the …

Cardiovascular diseases affect approximately 50 million people worldwide; thus, heart
disease prevention is one of the most important tasks of any health care system. Despite the …

Automatic detection of arrhythmia is of great significance for early prevention and diagnosis
of cardiovascular disease. Traditional feature engineering methods based on expert …