Abstract P3144: A Deep Neural Network Algorithm For Accurate Detection Of Brugada Syndrome Features In Electrocardiograms

Abstract

Brugada Syndrome (BrS) is a rare but potentially fatal cardiac condition that is difficult to diagnose due to the elusive nature of its characteristic ECG pattern. The diagnosis of BrS requires identification of a specific electrocardiographic (ECG) pattern, which often appears only upon administration of sodium-channel blockers. Here, we present a deep neural network (DNN) algorithm that accurately detects BrS features in ECGs without the need for a drug challenge. We trained and validated the DNN on two independent cohorts of patients who were enrolled in electrophysiological studies for BrS diagnosis. The internal prospective validation cohort included 370 subjects, and the multicenter external validation cohort included 110 subjects from three other medical institutions in Italy. All subjects who did not present a spontaneous type 1 ECG underwent an ajmaline drug challenge to aid in confirming a BrS diagnosis. We found that the DNN approach classified ECGs for BrS with a sensitivity of 79.6%, specificity of 93.6%, accuracy of 88.4%, and area under the curve (AUC) of 0.934±0.027 in the validation cohorts. The DNN model also detected BrS with 100% accuracy in all cases in which a patient presented the spontaneous type 1 pattern. Our results demonstrate that a multivariate machine-learning algorithm can accurately detect the features of BrS in a conventional ECG without the challenge of a sodium-channel blocker. The computer-assisted analysis of digital ECG traces from current electrocardiographs might help physicians recognize patients affected by this disease. In conclusion, we have developed an advanced deep learning algorithm that can accurately detect BrS features in ECGs. Our results demonstrate that the DNN approach could be an effective tool to improve the diagnosis of BrS and potentially prevent sudden cardiac death in affected individuals.