使用可解释的深度学习模型的血压估计,基于光电显微镜
Jade Perdereau1,2,3, Thibaut Chamoux2,3, Etienne Gayat1,3
1From the Université Paris Cité, INSERM UMRS 942 (MASCOT), Paris, France.
Anesthesia and analgesia
|December 16, 2024
概括
一个新的深度学习模型使用光电血压学 (PPG) 信号非侵入性地估计平均动脉压 (MAP). 这种准确的,连续的监测方法在没有侵入性方法的手术室中显示出有希望的结果.
科学领域:
- 生物医学工程 生物医学工程
- 医疗器械 医疗器械
- 人工智能在医学中的应用
背景情况:
- 动脉线提供卓越的血液动力学监测,但具有侵入性,不适用于常规使用.
- 需要持续的,非侵入性的动脉压监测,以克服间歇性袖口测量的局限性.
- 光电脉冲图 (PPG) 信号为持续的动脉压估计提供了一个潜在的非侵入性来源.
研究的目的:
- 开发和验证一个深度学习模型,从PPG信号中重建连续平均动脉压 (MAP).
- 为了将深度学习模型的性能与动脉线黄金标准进行比较.
- 评估非侵入性MAP估计作为临床环境中的决策支持工具的潜力.
主要方法:
- 使用卷积层和循环层的深度学习模型被训练在117名患者的高频PPG信号上.
- 输入特征包括PPG信号和衍生指标,如二突幅度和心率.
- 使用平均绝对误差 (MAE) 评估模型性能,并通过Grad-CAM突出度图评估可解释性.
主要成果:
- 深度学习模型的平均绝对误差 (MAE) 为3.5 (±4.4) mm Hg,比基线袖口模型 (MAE 6.1 ± 14.5 mm Hg) 提高了42.6%.
- 该模型符合国际准确性标准 (A级) 并显示了狭窄的置信区间.
- 格拉德-CAM分析表明,该模型主要利用来自PPG波形的二突区域的信息.
结论:
- 开发的深度学习模型使用PPG信号准确地估计了动脉压的非侵入性.
- 这项技术有可能成为手术室中一个有价值的决策支持工具,特别是当侵入性监测是不可行的时.
- 进一步融入临床工作流程可以提高患者监测和安全.
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