Heart Rhythm Center, Division of Cardiology, Department of Medicine, Taipei Veterans General Hospital, Taipei, Taiwan; Institute of Clinical Medicine and Faculty of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan.
Department of Industrial Engineering and Management, National Yang Ming Chiao Tung University, Hsinchu, Taiwan.
Can J Cardiol. 2022 Feb;38(2):152-159. doi: 10.1016/j.cjca.2021.08.014. Epub 2021 Aug 28.
Brugada syndrome is a major cause of sudden cardiac death in young people and has distinctive electrocardiographic (ECG) features. We aimed to develop a deep learning-enabled ECG model for automatic screening for Brugada syndrome to identify these patients at an early point in 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 1:1 allocation) were extracted from the hospital-based ECG database for a 2-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 with that of board-certified practicing cardiologists. The model was further validated in an independent ECG data set collected from hospitals in Taiwan and Japan.
The diagnoses by the deep learning model (area under the receiver operating characteristic curve [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 present the first deep learning-enabled ECG model for diagnosing Brugada syndrome, which appears to be a robust screening tool with a diagnostic potential rivalling trained physicians.
Brugada 综合征是年轻人心源性猝死的主要原因，具有独特的心电图（ECG）特征。我们旨在开发一种基于深度学习的 ECG 模型，用于 Brugada 综合征的自动筛查，以便在早期识别这些患者，从而进行救生治疗。
从医院的 ECG 数据库中提取了总共 276 份具有 1 型 Brugada ECG 模式的 ECG（276 份 1 型 Brugada ECG 和另 276 份随机检索的非 Brugada 类型 ECG 进行 1:1 分配），进行了 2 阶段分析，采用深度学习模型。在训练出识别右束支传导阻滞模式的网络后，我们将第一阶段的学习转移到第二项任务，以诊断 1 型 Brugada ECG 模式。将深度学习模型的诊断性能与经董事会认证的执业心脏病专家的诊断性能进行比较。该模型还在来自台湾和日本医院的独立 ECG 数据集上进行了验证。
深度学习模型的诊断（接受者操作特征曲线下的面积 [AUC] 0.96、敏感性 88.4%、特异性 89.1%）与标准诊断高度一致（kappa 系数 0.78）。然而，心脏病专家的诊断与标准诊断明显不同，一致性仅为中度（kappa 系数 0.63）。在独立的 ECG 队列中，深度学习模型仍然具有令人满意的诊断性能（AUC 0.89、敏感性 86.0%、特异性 90.0%）。
我们提出了第一个用于诊断 Brugada 综合征的基于深度学习的 ECG 模型，它似乎是一种强大的筛查工具，具有与训练有素的医生相当的诊断潜力。
