深度神经网络门检测用于基于加速度计的心脏监测
Ali Wajdan1,2, Vetle Christoffer Frostelid1,2, Manuel Villegas-Martinez1
1The Intervention Centre, Oslo University Hospital, Rikshospitalet, Oslo, Norway.
Scientific reports
|May 20, 2025
概括
一个新的深度神经网络使用心脏加速度计准确地检测二心事件. 这一进步可以通过对心跳的自动分析来改善功能性心脏监测.
科学领域:
- 生物医学工程 生物医学工程
- 心血管生理学心血管生理学
- 机器学习在医学中的应用
背景情况:
- 植入的心脏加速度计提供持续的心脏功能监测.
- 准确检测心脏门事件对于导出功能指数至关重要.
- 之前的工作建立了一个神经网络用于大动脉事件检测.
研究的目的:
- 训练和验证一个深度神经网络,以检测中枢关闭 (MVC) 和打开 (MVO) 时间.
- 从检测到的门事件中提取和评估功能性心脏指数.
- 评估这些指数与心脏功能变化的相关性.
主要方法:
- 通过训练深度神经网络,从加速度计数据中识别MVC和MVO.
- 该网络测试了46只动物的约5900次心跳.
- 根据检测到的门事件,提取了四个功能指数.
主要成果:
- 神经网络在MVO检测方面实现了89.6%的准确性,在MVC检测方面达到87.5%.
- 门事件检测的平均绝对误差为13毫秒.
- 提取的功能指数显示与左心室中风工作有很强的相关性 (0.67 < r < 0.84).
结论:
- 使用深度学习,自动检测中枢事件是可行的.
- 这种方法提高了心脏加速度计的实用性,以改善心脏监测.
- 衍生出来的功能指数有效地反映了心脏功能的变化.
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