使用基于心电图的深度学习预测和识别药物诱导的I型布鲁加达模式
Paul-Adrian Călburean1,2, Luigi Pannone1, Cinzia Monaco1
1Heart Rhythm Management Centre, Postgraduate Program in Cardiac Electrophysiology and Pacing Universitair Ziekenhuis Brussel, Vrije Universiteit Brussel, European Reference Networks Guard-Heart Brussels Belgium.
Journal of the American Heart Association
|May 10, 2024
概括
一个深层卷积神经网络,BrS-Net,准确地识别了布鲁加达综合征 (BrS) 的I型模式. BrS-Net还从基线心电图中预测了BrS的发展,有助于预防心脏突然死亡.
科学领域:
- 心脏病学 心脏病学
- 人工智能的人工智能
- 医学诊断 医学诊断 医学诊断
背景情况:
- 布鲁加达综合征 (BrS) 与健康个体的突然心脏死亡有关.
- 药物诱导的BrS占所有BrS病例的很大一部分 (55-70%).
研究的目的:
- 开发和评估用于布鲁加达综合征诊断的深 convolutional 神经网络 (CNN).
- 评估CNN识别和预测BrS类型I模式的能力.
主要方法:
- 波形分析从基线和阿贾马林输液中转换了心电图跟踪.
- 一个称为BRS-Net的CNN受过训练,能够识别和预测BRS I型模式.
- 该研究包括1188名接受阿贾马林测试的患者.
主要成果:
- 在阿贾马林期间,BrS-Net在识别BrS I型模式方面取得了很高的性能 (AUC-ROC 0.945,AUC-PR 0.892).
- 从基线心电图 (AUC-ROC 0.805,AUC-PR 0.605) 来看,BrS-Net显示了从基线心电图 (AUC-ROC 0.805,AUC-PR 0.605) 来看BrS I型发育的良好预测性能.
- 30.3%的患者在阿贾马林输液期间出现了BrS型I模式.
结论:
- BrS-Net有效地识别了高准确度的 BrS I 类型模式.
- BrS-Net在预测未经选择的人群中基线ECG的BrS发展方面表现有前途.
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