在长期心电图记录中预测即将发生的心房动
Sydney R Rooney1, Roman Kaufman2, Raghavan Murugan3
1Department of Pediatrics, Children's Hospital of Pittsburgh, 4401 Penn Ave, Pittsburgh, PA 15224, USA.
Journal of electrocardiology
|September 8, 2023
概括
深度学习模型现在可以使用心电图数据预测即将出现的心房动 (AF),为患者的护理提供关键的带头时间. 在预测AF方面的这一突破可以显著改善住院患者的管理和结果.
科学领域:
- 心脏病学 心脏病学
- 人工智能的人工智能
- 医疗信息学 医疗信息学
背景情况:
- 急性心房动 (AF) 具有显著的发病率,但缺乏即将开始的预测模型.
- 目前的住院护理缺乏预测AF即时发生的工具.
- 预测AF发作对于及时干预和改善患者结果至关重要.
研究的目的:
- 评估深度学习在预测迫在眉的心房动 (AF) 发作方面的能力.
- 使用心电图数据实现AF预测临床相关的预测时间.
- 探索神经网络在治疗住院患者的AF中的潜力.
主要方法:
- 使用了Physiobank长期AF数据库与24小时标记的心电图记录.
- 开发了三种深度学习模型 (卷积层和变压层) 用于AF预测.
- 评估模型使用AUC(t),精度回忆曲线和风险轨迹在不同的交付时间 (7.5-60分钟).
主要成果:
- 所有模型都显示了AF发作前大约15分钟风险轨迹的分歧.
- 鼻节律模型在7.5分钟的时间内实现了最高的AUC (0.74).
- 使用所有先前的波形数据的模型显示,在较长的交付时间内,更高的AUC显示了可比性能.
结论:
- 证明了神经网络在长期心电图记录中预测AF发病的潜力,并具有临床相关的领先时间.
- 这项概念验证研究强调了深度学习对AF预测的有用性.
- 临床部署需要在较大的队列中进行外部验证.
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