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

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 …

The promise of artificial intelligence (AI) to improve and reduce inequities in access, quality,
and appropriateness of high-quality diagnosis remains largely unfulfilled. Vast clinical data …

Background: Myocardial ischemia is a common early symptom of cardiovascular disease
(CVD). Reliable detection of myocardial ischemia using computer-aided analysis of …

Objective. Learning to classify cardiac abnormalities requires large and high-quality labeled
datasets, which is a challenge in medical applications. Small datasets from various sources …

Deep learning has made many advances in data classification using electrocardiogram
(ECG) waveforms. Over the past decade, data science research has focused on developing …

The first ACM/IEEE TinyML Design Contest (TDC) held at the 41st International Conference
on Computer-Aided Design (ICCAD) in 2022 is a challenging, multimonth, research and …

Objective. As cardiovascular diseases are a leading cause of death, early and accurate
diagnosis of cardiac abnormalities for a lower cost becomes particularly important. Given …

Traditionally, machine learning-based clinical prediction models have been trained and
evaluated on patient data from a single source, such as a hospital. Cross-validation methods …

Although time-series classification has many applications in healthcare and manufacturing,
the high cost of data collection and labeling hinders its widespread use. To reduce data …