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Predicting and Recognizing Drug‐Induced Type I Brugada Pattern Using ECG‐Based Deep Learning
- Source :
- Journal of the American Heart Association: Cardiovascular and Cerebrovascular Disease, Vol 13, Iss 10 (2024)
- Publication Year :
- 2024
- Publisher :
- Wiley, 2024.
-
Abstract
- Background Brugada syndrome (BrS) has been associated with sudden cardiac death in otherwise healthy subjects, and drug‐induced BrS accounts for 55% to 70% of all patients with BrS. This study aims to develop a deep convolutional neural network and evaluate its performance in recognizing and predicting BrS diagnosis. Methods and Results Consecutive patients who underwent ajmaline testing for BrS following a standardized protocol were included. ECG tracings from baseline and during ajmaline were transformed using wavelet analysis and a deep convolutional neural network was separately trained to (1) recognize and (2) predict BrS type I pattern. The resultant networks are referred to as BrS‐Net. A total of 1188 patients were included, of which 361 (30.3%) patients developed BrS type I pattern during ajmaline infusion. When trained and evaluated on ECG tracings during ajmaline, BrS‐Net recognized a BrS type I pattern with an AUC‐ROC of 0.945 (0.921–0.969) and an AUC‐PR of 0.892 (0.815–0.939). When trained and evaluated on ECG tracings at baseline, BrS‐Net predicted a BrS type I pattern during ajmaline with an AUC‐ROC of 0.805 (0.845–0.736) and an AUC‐PR of 0.605 (0.460–0.664). Conclusions BrS‐Net, a deep convolutional neural network, can identify BrS type I pattern with high performance. BrS‐Net can predict from baseline ECG the development of a BrS type I pattern after ajmaline with good performance in an unselected population.
Details
- Language :
- English
- ISSN :
- 20479980
- Volume :
- 13
- Issue :
- 10
- Database :
- Directory of Open Access Journals
- Journal :
- Journal of the American Heart Association: Cardiovascular and Cerebrovascular Disease
- Publication Type :
- Academic Journal
- Accession number :
- edsdoj.46260e4e9a2e4e71b5c2609a5d3c3113
- Document Type :
- article
- Full Text :
- https://doi.org/10.1161/JAHA.123.033148