| Autor: |
Paul‐Adrian Călburean, Luigi Pannone, Cinzia Monaco, Domenico Della Rocca, Antonio Sorgente, Alexandre Almorad, Gezim Bala, Filippo Aglietti, Robbert Ramak, Ingrid Overeinder, Erwin Ströker, Gudrun Pappaert, Marius Măru’teri, Marius Harpa, Mark La Meir, Pedro Brugada, Juan Sieira, Andrea Sarkozy, Gian‐Battista Chierchia, Carlo de Asmundis |
| Jazyk: |
angličtina |
| Rok vydání: |
2024 |
| Předmět: |
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| Zdroj: |
Journal of the American Heart Association: Cardiovascular and Cerebrovascular Disease, Vol 13, Iss 10 (2024) |
| Druh dokumentu: |
article |
| ISSN: |
2047-9980 |
| DOI: |
10.1161/JAHA.123.033148 |
| Popis: |
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. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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