Prediction of arrhythmias based on 10-second 12-lead ECG signals
Description: Heart arrhythmia such as atrial fibrillation can pose a serious health risk for patients. The early detection and monitoring are crucial to prevent complications. This project develops AI models with advanced deep learning techniques to improve risk patient screening and predict disease progression. Longitudinal ECG data analysis can help to identify subtle rhythm patterns, allowing abnormalities to be detected early, even before clinical symptoms appear.
Funder: Vlaamse regering
Funding type: fellowship
Funder scheme: BOF - doctoral mandates
Start date: 2025-09-01
End date: 2026-03-31
Investigator: Dirk Deschrijver
Institution: UGent
Funded outputs count: 0
Funder award ID: BOF25/CDV/057
OpenAlex ID: G5412233733