Cardiac arrhythmia has been identified as a type of cardiovascular diseases (CVDs) that
causes approximately 12% of all deaths globally. The development of Internet-of-Things has …

The classification of heartbeats is an important method for cardiac arrhythmia analysis. This
study proposes a novel heartbeat classification method using hybrid time-frequency analysis …

Wearable devices are increasingly popular for health monitoring via electrocardiograms
(ECGs) as they can portably monitor heart conditions over a long time. However, so far there …

Electrocardiogram (ECG) analysis is widely used in the diagnosis of cardiovascular
diseases. This article proposes an explainable rule-mining strategy for prioritizing abnormal …

ECG (Electrocardiogram), the most commonly used tool in the diagnosis of cardiac
diseases, contains a large amount of physiologic information about the electrical activity of …

In this paper, we investigate the applicability of the permutation entropy (PE) and the
conditional entropy of ordinal patterns (CEOP) to Electrocardiogram (ECG) data analysis …

Cardiovascular disease is the leading cause of death in the world, so early detection of heart
conditions is very important. Detection related to cardiovascular disease can be conducted …

Diabetic retinopathy is a diabetes complication that effects eyes. It disrupts the vasculature of
the sensitive tissue present at the back of the eye. If this complication is untreated it may lead …

We propose a deep neural architecture that performs uncertainty-aware multi-view
classification of arrhythmia from ECG. Our method learns two different views (1D and 2D) of …

The automatic and rapid analysis of long-term electrocardiogram (ECG) records still remains
a challenging task. Most of the existing algorithms are time consuming and require a training …