Active learning (AL) attempts to maximize a model's performance gain while annotating the
fewest samples possible. Deep learning (DL) is greedy for data and requires a large amount …

Cardiovascular disease (CVD) is a general term for a series of heart or blood vessels
abnormality that serves as a global leading reason for death. The earlier the abnormal heart …

The correct prediction of heart disease can prevent life threats, and incorrect prediction can
prove to be fatal at the same time. In this paper different machine learning algorithms and …

Cardiovascular disease is a substantial cause of mortality and morbidity in the world. In
clinical data analytics, it is a great challenge to predict heart disease survivor. Data mining …

Computerized electrocardiogram (ECG) interpretation plays a critical role in the clinical ECG
workflow. Widely available digital ECG data and the algorithmic paradigm of deep learning …

In the last few years, the application of Machine Learning approaches like Deep Neural
Network (DNN) models have become more attractive in the healthcare system given the …

The role of automatic electrocardiogram (ECG) analysis in clinical practice is limited by the
accuracy of existing models. Deep Neural Networks (DNNs) are models composed of …

This article presents a new deep learning approach for cardiac arrhythmia (17 classes)
detection based on long-duration electrocardiography (ECG) signal analysis …

Electroencephalography (EEG)-based brain-computer interfaces (BCIs), particularly those
using motor-imagery (MI) data, have the potential to become groundbreaking technologies …

The prediction of cardiac disease helps practitioners make more accurate decisions
regarding patients' health. Therefore, the use of machine learning (ML) is a solution to …