Background Heart disease diagnosis is a challenging task and it is important to explore
useful information from the massive amount of electrocardiogram (ECG) records of patients …

Visual transformers have recently gained popularity in the computer vision community as
they began to outrank convolutional neural networks (CNNs) in one representative visual …

Background: Cardiovascular diseases (CVDs) are a leading cause of death worldwide.
Deep learning methods have been widely used in the field of medical image analysis and …

Deep learning-based intelligent electrocardiogram (ECG) diagnosis algorithms heavily rely
on large annotated datasets. Unfortunately, in the context of ECG diagnosis, privacy issues …

Electrocardiography (ECG), an electrical measurement which captures cardiac activities, is
the gold standard for diagnosing cardiovascular disease (CVD). However, ECG is infeasible …

Cardiac arrhythmia can lead to morbidity and mortality and is a substantial economic
burden. Electrocardiogram (ECG) monitoring is widely used to detect arrhythmia. The …

The development of robust artificial intelligence models for echocardiography has been
limited by the availability of annotated clinical data. Here, to address this challenge and …

Background and objective Long-term electrocardiogram monitoring comes at the expense of
signal quality. During unconstrained movements, the electrocardiogram is often corrupted by …

Cardiac disease is the leading cause of death in the US. Accurate heart disease detection is
critical to timely medical treatment to save patients' lives. Routine use of the …

Electrocardiograms (ECGs) play a vital role in the clinical diagnosis of heart diseases. An
ECG record of the heart signal over time can be used to discover numerous arrhythmias. Our …