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

Authors:
  • CLChih-Min Liu
    Chih-Min Liu
  • CLChien-Liang Liu
    Chien-Liang Liu
  • KHKai-Wen Hu
    Kai-Wen Hu
  • VTVincent S. Tseng
    Vincent S. Tseng
  • SCShih-Lin Chang
    Shih-Lin Chang
  • YLYenn-Jiang Lin
    Yenn-Jiang Lin
  • LLLi-Wei Lo
    Li-Wei Lo
  • FCFa-Po Chung
    Fa-Po Chung
  • TCTze-Fan Chao
    Tze-Fan Chao
  • TTTa-Chuan Tuan
    Ta-Chuan Tuan
  • JLJo-Nan Liao
    Jo-Nan Liao
  • CLChin-Yu Lin
    Chin-Yu Lin
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Abstract
BACKGROUND Brugada syndrome is a rare inherited arrhythmia with a unique electrocardiogram (ECG) pattern (type 1 Brugada ECG pattern), which is a major cause of sudden cardiac death in young people. Automatic screening for the ECG pattern of Brugada syndrome by a deep learning model gives us the chance to identify these patients at an early time, thus allowing them to receive life-saving therapy. OBJECTIVE To develop a deep learning-enabled ECG model for diagnosing Brugada syndrome. METHODS 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. We first trained the network to identify right bundle branch block (RBBB) pattern, and then, 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 also validated by the independent international data of ECGs. RESULTS The AUC (area under the curve) of the deep learning model in diagnosing the type 1 Brugada ECG pattern was 0.96 (sensitivity: 88.4%, specificity: 89.1%). The sensitivity and specificity of the cardiologists for the diagnosis of the type 1 Brugada ECG pattern were 62.7±17.8%, and 98.5±3.0%, respectively. The diagnoses by the deep learning model were highly consistent with the standard diagnoses (Kappa coefficient: 0.78, McNemar test, P = .86). However, the diagnoses by the cardiologists were significantly different from the standard diagnoses, with only moderate consistency (Kappa coefficient: 0.60, McNemar test, P = 2.35x10-22). For the international validation, the AUC of the deep learning model for diagnosing the type 1 Brugada ECG pattern was 0.99 (sensitivity: 85.7%, specificity: 100.0%). CONCLUSIONS We presented the first deep learning-enabled ECG model for diagnosing Brugada syndrome, which is a robust screening tool with better diagnostic sensitivity than that of cardiologists. CLINICALTRIAL
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