Artificial intelligence (AI) is increasingly used in electrocardiography (ECG) to assist in
diagnosis, stratification, and management. AI algorithms can help clinicians in the following …

The integration of machine/deep learning and sensing technologies is transforming
healthcare and medical practice. However, inherent limitations in healthcare data, namely …

The analysis and processing of ECG signals are a key approach in the diagnosis of
cardiovascular diseases. The main field of work in this area is classification, which is …

One of the most critical steps when diagnosing cardiovascular disorders is examining and
processing ECG data. Classification of health and ill persons is the primary focus of research …

Diagnosing COVID-19 accurately and rapidly is vital to control its quick spread, lessen
lockdown restrictions, and decrease the workload on healthcare structures. The present …

Cardiovascular diseases (CVDs) are a group of heart and blood vessel disorders that is one
of the most serious dangers to human health, and the number of such patients is still …

Learning information-rich and generalizable representations effectively from unlabeled
multivariate cardiac signals to identify abnormal heart rhythms (cardiac arrhythmias) is …

Deep Neural Networks (DNNs) are state-of-the-art machine learning algorithms, the
application of which in electrocardiographic signals is gaining importance. So far, limited …

Abstract The 3D Discrete Wavelet Transform (DWT) and Support Vector Machine (SVM) are
used in this study to analyze and characterize Electrocardiogram (ECG) signals. This …

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 …