Abstract:Congenital or drug-induced long-QT syndromes can cause Torsade-de-Pointes (TdP), a life-threatening ventricular arrhythmia. The current strategy to identify individuals at high risk of TdP consists on measuring the QT duration on the electrocardiogram (ECG), shown to provide limited information. We propose an original method, including training deep neural networks to recognize ECG alterations induced by QT-prolonging drugs, as a comprehensive evaluation of TdP risk. These models accurately detected patients t… Show more
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