. We developed a deep-learning model that reconstructs continuous mean arterial pressure (MAP) using the photoplethysmograhy (PPG) signal and compared it to the arterial line gold standard. METHODS: We analyzed high-frequency PPG signals from 117 patients in neuroradiology and digestive surgery with a median of 2201 (interquartile range [IQR], 788–4775) measurements per patient. We compared models with different combinations of convolutional and recurrent layers using as inputs for our neural network high-frequency PPG and derived features including dicrotic notch relative amplitude, perfusion index, and heart rate. Mean absolute error (MAE) was used as performance metrics. Explainability of the deep-learning model was reconstructed with Grad-CAM, a visualization technique using saliency maps to highlight the parts of an input that are significant for a deep-learning model decision-making process. RESULTS: An MAP baseline model, which consisted only of standard cuff measures, reached an MAE of 6.1 (± 14.5) mm Hg. In contrast, the deep-learning model achieved an MAE of 3.5 (± 4.4) mm Hg on the external test set (a 42.6% improvement). This model also achieved the narrowest confidence intervals and met international standards used within the community (grade A). The saliency map revealed that the deep-learning model primarily extracts information near the dicrotic notch region. CONCLUSIONS: Our deep-learning model noninvasively estimates arterial pressure with high accuracy. This model may show potential as a decision-support tool in operating-room settings, particularly in scenarios where invasive blood pressure monitoring is unavailable....

中文翻译：

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使用基于光电容积脉搏波的可解释深度学习模型估计血压


.我们开发了一个深度学习模型，该模型使用光电容积脉搏 （PPG） 信号重建连续平均动脉压 （MAP），并将其与动脉线金标准进行比较。方法： 我们分析了 117 例神经放射学和消化外科患者的高频 PPG 信号，每例患者的中位数为 2201 （四分位距 [IQR]，788-4775）。我们比较了具有不同卷积层和循环层组合的模型，用作神经网络高频 PPG 的输入，并衍生特征包括二分切迹相对振幅、灌注指数和心率。平均绝对误差 （MAE） 用作性能指标。深度学习模型的可解释性是使用 Grad-CAM 重建的，Grad-CAM 是一种可视化技术，使用显著性图来突出输入中对深度学习模型决策过程具有重要意义的部分。结果： 仅由标准袖带措施组成的 MAP 基线模型达到 6.1 （± 14.5） mm Hg。相比之下，深度学习模型在外部测试集上的 MAE 为 3.5 （± 4.4） mm Hg（提高了 42.6%）。该模型还实现了最窄的置信区间，并符合社区内使用的国际标准（A 级）。显著性图显示，深度学习模型主要提取二分缺口区域附近的信息。结论： 我们的深度学习模型无创估计动脉压，精度高。该模型可能在手术室环境中显示出作为决策支持工具的潜力，尤其是在无法进行有创血压监测的情况下。