Classifying electrocardiogram (ECG) heartbeats for arrhythmic risk prediction is a
challenging task due to minute variations in the amplitude, duration and morphology of the …

ECG signals reflect all the electrical activities of the heart. Consequently, it plays a key role
in the diagnosis of the cardiac disorder and arrhythmia detection. Based on tiny alterations …

Accurate classification of cardiac arrhythmias is a crucial task because of the non-stationary
nature of electrocardiogram (ECG) signals. In a life-threatening situation, an automated …

Computer-aided heartbeat classification has a significant role in the diagnosis of cardiac
dysfunction. Electrocardiogram (ECG) provides vital information about the heartbeats. In this …

Ventricular fibrillation (VF) is a life-threatening cardiac arrhythmia. A high impulse current is
required in this stage to save lives. In this paper, an empirical mode decomposition (EMD) …

Deep learning has achieved promising results on a broad spectrum of tasks using an end-to-
end approach, and domain-specific knowledge can be used to supplement it by either …

It is a difficult process to detect abnormal heart beats, known as arrhythmia, in long-term
ECG recording. Thus, computer-aided diagnosis systems have become a supportive tool for …

Arrhythmia is a cardiac condition caused by abnormal electrical activity of the heart, and an
electrocardiogram (ECG) is the non-invasive method used to detect arrhythmias or heart …

Heart diseases are one of the most important death causes across the globe. Therefore,
early detection of heart diseases is crucial to reduce the rising death rate. Electrocardiogram …

Automatic detection of different cardiac abnormalities is an emerging field of study in
assistive diagnosis technology for ca rdiac diseases. Atrial fibrillation (AF) is a kind of …