使用弹性心脏图谱估计心脏输出量:在健康受试者身上进行可行性研究
Johannes Nordsteien Svensøy1,2, Erik Alonso3, Andoni Elola4
1Norwegian National Advisory Unit on Prehospital Emergency Medicine (NAKOS), Division of Prehospital Services, Oslo University Hospital, Oslo, Norway.
Scientific reports
|January 18, 2024
概括
非侵入性弹性心电图 (BCG) 显示了通过分析信号特征来估计心脏输出 (CO) 的潜力. 这种自动化,非侵入性的方法可以通过可靠地跟踪血液循环来帮助医院前的急诊医疗.
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
- 医疗器械 医疗器械 医疗器械
背景情况:
- 缺乏可靠的,非侵入性循环监测,用于医院前的急诊医学.
- 目前的心脏输出 (CO) 监测器对于医院前使用是不切实际的.
- 弹性心电图 (BCG) 提供了一种非侵入性方法来评估心脏收缩性和跟踪CO变化.
研究的目的:
- 评估使用非侵入性BCG信号的形态特征估计CO的可行性.
- 根据BCG数据开发和验证用于CO估计的机器学习模型.
- 探索BCG的潜力,作为一个可靠的,自动化的CO监测解决方案,用于医院前的设置.
主要方法:
- 在20名健康受试者中记录了心电图,动脉/腹腔BCG和侵入性动脉血压.
- 处理BCG信号以分离循环成分 (CCc,CCa) 并提取66个节拍对节拍的形态特征.
- 使用75%的开发集开发了一个机器学习模型,并在25%的验证集上验证了性能.
主要成果:
- 机器学习模型实现了 CO 估计的平均绝对误差为 0.83 L/min.
- 该模型在验证集中显示了30.2%的百分比误差和95%的协议极限 -2.18-1.89 L/min.
- BCG分析显示了可靠的CO估计和跟踪的潜力.
结论:
- 基于BCG的CO估计是一种有前途的非侵入性方法.
- 这种方法是迈向用于医院前紧急情况的自动化,可靠的CO监测的重要一步.
- 需要进一步的测试,以验证BCG在现实世界的医院前场景中的实用性.
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