A deep learning approach identifies new ECG features in congenital long QT syndrome.

ABSTRACT:

Background

Congenital long QT syndrome (LQTS) is a rare heart disease caused by various underlying mutations. Most general cardiologists do not routinely see patients with congenital LQTS and may not always recognize the accompanying ECG features. In addition, a proportion of disease carriers do not display obvious abnormalities on their ECG. Combined, this can cause underdiagnosing of this potentially life-threatening disease.

Methods

This study presents 1D convolutional neural network models trained to identify genotype positive LQTS patients from electrocardiogram as input. The deep learning (DL) models were trained with a large 10-s 12-lead ECGs dataset provided by Amsterdam UMC and externally validated with a dataset provided by University Hospital Leuven. The Amsterdam

SUBMITTER: Aufiero S 

PROVIDER: S-EPMC9063181 | biostudies-literature | 2022 May

REPOSITORIES: biostudies-literature