Electrocardiogram (ECG) recordings are indicative for the state of the human heart.
Automatic analysis of these recordings can be performed using various computational …

The complexity of cardiac electrophysiology, involving dynamic changes in numerous
components across multiple spatial (from ion channel to organ) and temporal (from …

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 …

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 …

Artificial Intelligence (AI) performs human intelligence-dependant tasks using tools such as
Machine Learning, and its subtype Deep Learning. AI has incorporated itself in the field of …

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 …

Cardiovascular diseases (CVDs), including asymptomatic myocardial ischemia, angina,
myocardial infarction, and ischemic heart failure, are the leading cause of death globally …

Electrocardiograms (ECGs) are widely used to clinically detect cardiac arrhythmias (CAs).
They are also being used to develop computer-assisted methods for heart disease …

The electrocardiogram is the most widely used diagnostic tool that records the electrical
activity of the heart and, therefore, its use for identifying markers for early diagnosis and …

Background: The ECG remains the most widely used diagnostic test for characterization of
cardiac structure and electrical activity. We hypothesized that parallel advances in …