Heart

Prognostic importance of exercise electrocardiography in patients with suspected microvascular disease

Background Coronary microvascular dysfunction (CMD) is associated with a poor prognosis but is difficult to diagnose non-invasively. In a recent paper, ST-segment depression on exercise-ECG was found ...
Journal of Medical Internet Research

Deep Learning–Based Electrocardiogram Model (EIANet) to Predict Emergency Department Cardiac Arrest: Development and External Validation Study

The feature map showed that the region of interest in the ECG was the ST segment. Conclusions: EIANet demonstrates promising potential for accurately predicting EDCA using triage ECG images, offering ...
IEEE

A Multi-Scale Deep Learning Framework Combining MobileViT-ECA and LSTM for Accurate ECG Analysis

Abstract: Electrocardiogram (ECG) analysis is crucial for diagnosing cardiovascular diseases (CVD), especially atrial fibrillation (AF), a prevalent cardiac rhythm abnormality. However, the ...
IEEE

ECG-Mamba: Cardiac Abnormality Classification With Non-Uniform-Mix Augmentation on 12-Lead ECGs

Abstract: Objective: The detection of heart abnormalities using electrocardiograms (ECG) is a critical task in medical diagnostics. A lot of literature has utilized ResNet and Transformer ...
Medical Xpress

AI model detects prediabetes using ECG data without need for blood tests

DiaCardia, a novel artificial intelligence model that can accurately identify individuals with prediabetes using either 12-lead or single-lead electrocardiogram (ECG) data, has been developed. This ...