Chest pain is among the most common reasons for presentation to the emergency department, yet identifying patients at the ...
Abstract: Objective: We present the first multimodal deep learning framework combining ultrasound (US) and electrocardiography (ECG) data to predict cardiac quiescent periods (QPs) for optimized ...
Abstract: Objective: In this paper we develop and evaluate ECG-SMART-NET for occlusion myocardial infarction (OMI) identification. OMI is a severe form of heart attack characterized by complete ...
An investigation has found a "concerning level of variation" in how 12-lead electrocardiogram (ECG) skills are taught in UK undergraduate paramedic programmes. A report by the Health Services Safety ...
An artificial intelligence (AI) deep-learning algorithm interpreting preoperative ECGs can identify risk for postoperative death in those undergoing cardiac surgery, noncardiac surgery, and ...
In a recent article published in Npj Digital Medicine, researchers utilized electrocardiogram (ECG) data from a large retrospective cohort to extract various heart rate variability (HRV) measures.
Timely diagnosis of structural heart disease improves patient outcomes, yet many remain underdiagnosed. While population screening with echocardiography is impractical, ECG-based prediction models can ...
Extracting the foetal signal from the mother’s abdominal electrocardiogram is a crucial step for monitoring the health of the unborn child. (Courtesy: iStock/iTie) Researchers in Iran have used a deep ...
Machine-learning models that incorporate common clinical features along with ECG findings may help identify patients who are likely to have coronary artery calcium (CAC), with potential implications ...