Sign in
Ctrl K

Predicting the Brugada ECG without ajmaline

Predicting the Brugada syndrome using machine learning

Sudden cardiac death accounts for 15% to 20% of all deaths. In cases of sudden unexplained death or cardiac arrest establishing the correct diagnosis is a prerequisite to identifying family members at risk and initiating preventive measures.

In the Brugada syndrome the diagnosis is often established based on the presence of a spontaneous or drug induced (e.g. ajmaline a cardiac sodium channel blocker) Brugada-pattern on the electrocardiogram (ECG). Although complications associated with the procedure are rare, there is a risk of cardiac arrhythmia during the procedure. This project aims at to predicting the occurrence of the drug induced Brugada-pattern using baseline ECGs only.

The project team will use advanced AI methods, such as deep-learning convolutional neural networks, where the features are learned by the network itself, thus potentially allowing the discovery of new ECG features associated with the specific phenotype, genotype or in the case of this project response to a drug challenge.

Participating organisations

Amsterdam University Medical Centers
Netherlands eScience Center
Life Sciences
Life Sciences

Team

EL
Elisabeth Lodder
SA
Simona Aufiero
Sonja Georgievska
Sonja Georgievska
Lead RSE
Netherlands eScience Center
Cunliang Geng
eScience Research Engineer
Netherlands eScience Center
Leon Oostrum
Leon Oostrum
eScience Research Engineer
Netherlands eScience Center
Jisk Attema
Programme Manager
Netherlands eScience Center

Related software

mcfly

MC

Helps you find a suitable neural network configuration for deep learning on time series.

Updated 18 months ago
37 12