Ronan Robert, Tarabanis Constantine, Chinitz Larry, Jankelson Lior
Leon H. Charney Division of Cardiology, Cardiac Electrophysiology, NYU Langone Health, New York University School of Medicine, New York City, NY, USA.
Sci Rep. 2025 Mar 19;15(1):9396. doi: 10.1038/s41598-025-94130-x.
Existing deep learning algorithms for electrocardiogram (ECG) classification rely on supervised training approaches requiring large volumes of reliably labeled data. This limits their applicability to rare cardiac diseases like Brugada syndrome (BrS), often lacking accurately labeled ECG examples. To address labeled data constraints and the resulting limitations of supervised training approaches, we developed a novel deep learning model for BrS ECG classification using the Variance-Invariance-Covariance Regularization (VICReg) architecture for self-supervised pre-training. The VICReg model outperformed a state-of-the-art neural network in all calculated metrics, achieving an area under the receiver operating and precision-recall curves of 0.88 and 0.82, respectively. We used the VICReg model to identify missed BrS cases and hence refine the previously underestimated institutional BrS prevalence and patient outcomes. Our results provide a novel approach to rare cardiac disease identification and challenge existing BrS prevalence estimates offering a framework for other rare cardiac conditions.
现有的用于心电图（ECG）分类的深度学习算法依赖于需要大量可靠标记数据的监督训练方法。这限制了它们在诸如Brugada综合征（BrS）等罕见心脏病中的应用，因为这类疾病往往缺乏准确标记的ECG示例。为了解决标记数据的限制以及监督训练方法由此产生的局限性，我们开发了一种用于BrS心电图分类的新型深度学习模型，该模型使用方差-不变性-协方差正则化（VICReg）架构进行自监督预训练。在所有计算指标上，VICReg模型均优于一个先进的神经网络，在接收者操作特征曲线下面积和精确召回率曲线下面积分别达到了0.88和0.82。我们使用VICReg模型来识别漏诊的BrS病例，从而完善先前被低估的机构BrS患病率和患者预后。我们的结果为罕见心脏病的识别提供了一种新方法，并对现有的BrS患病率估计提出了挑战，为其他罕见心脏病提供了一个框架。
