Gonzalez Jose M, Vega Saul J, Mosely Shakayla V, Pascua Stefany V, Rodgers Tina M, Snider Eric J
U.S. Army Institute of Surgical Research, JBSA Fort Sam Houston, San Antonio, TX 78234, USA.
Sensors (Basel). 2025 Aug 13;25(16):5035. doi: 10.3390/s25165035.
We aimed to evaluate the non-invasive photoplethysmography waveform as a means to predict mean arterial pressure using artificial intelligence models. This was performed using datasets captured in large animal hemorrhage and resuscitation studies. An initial deep learning model trained using a subset of large animal data and was then evaluated for real-time blood pressure prediction. With the successful proof-of-concept experiment, we further tested different feature extraction approaches as well as different machine learning and deep learning methodologies to examine how various combinations of these methods can improve the accuracy of mean arterial pressure predictions from a non-invasive photoplethysmography sensor. Different combinations of feature extraction and artificial intelligence models successfully predicted mean arterial pressure throughout the study. Overall, manual feature extraction fed into a long short-term memory network tracked the mean arterial pressure through hemorrhage and resuscitation with the highest accuracy.
我们旨在评估无创光电容积脉搏波描记术波形，作为使用人工智能模型预测平均动脉压的一种手段。这是通过在大型动物出血和复苏研究中捕获的数据集来进行的。最初使用大型动物数据的一个子集训练了一个深度学习模型，然后对其进行实时血压预测评估。通过成功的概念验证实验，我们进一步测试了不同的特征提取方法以及不同的机器学习和深度学习方法，以研究这些方法的各种组合如何提高来自无创光电容积脉搏波描记术传感器的平均动脉压预测的准确性。在整个研究中，特征提取和人工智能模型的不同组合成功地预测了平均动脉压。总体而言，输入到长短期记忆网络中的手动特征提取在出血和复苏过程中以最高的准确性跟踪了平均动脉压。
