A Deep Learning-enabled Electrocardiogram Model for the Identification of a Rare Inherited Arrhythmia: Brugada Syndrome.

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Abstract

Brugada syndrome is a major cause of sudden cardiac death in young people with a distinctive electrocardiogram (ECG) feature. We aimed to develop a deep learning-enabled ECG model for automatic screening Brugada syndrome to identify these patients at an early time, thus allowing for life-saving therapy.A total of 276 ECGs with a type 1 Brugada ECG pattern (276 type 1 Brugada ECGs and another randomly retrieved 276 non-Brugada type ECGs for one to one allocation) were extracted from the hospital-based ECG database for a two-stage analysis with a deep learning model. After trained network for identifying right bundle branch block pattern, we transferred the first-stage learning to the second task to diagnose the type 1 Brugada ECG pattern. The diagnostic performance of the deep learning model was compared to that of board-certified practicing cardiologists. The model was further validated in the independent ECG dataset, collected from the hospitals in Taiwan and Japan.The diagnoses by the deep learning model (AUC: 0.96, sensitivity: 88.4%, specificity: 89.1%) were highly consistent with the standard diagnoses (Kappa coefficient: 0.78). However, the diagnoses by the cardiologists were significantly different from the standard diagnoses, with only moderate consistency (Kappa coefficient: 0.63). In the independent ECG cohort, the deep learning model still reached a satisfactory diagnostic performance (AUC 0.89, sensitivity: 86.0%, specificity: 90.0%).We presented the first deep learning-enabled ECG model for diagnosing Brugada syndrome, which appears to be a robust screening tool with a diagnostic potential rivaling trained physicians.Copyright © 2021. Published by Elsevier Inc.