Chen Ruijuan, He Ming, Xiao Shumian, Wang Cong, Wang Huiquan, Xu Jiameng, Zhang Jun, Zhang Guang
School of Life Sciences, TianGong University, Tianjin, China.
Tianjin Key Laboratory of Quality Control and Evaluation Technology for Medical Devices, Tianjin, China.
Front Physiol. 2023 Jul 27;14:1180631. doi: 10.3389/fphys.2023.1180631. eCollection 2023.
The purpose of this study is to identify the blood pressure variation, which is important in continuous blood pressure monitoring, especially in the case of low blood volume, which is critical for survival. A pilot study was conducted to identify blood pressure variation with hypovolemia using five Landrace pigs. New multi-dimensional morphological features of Photoplethysmography (PPG) were proposed based on experimental study of hemorrhagic shock in pigs, which were strongly correlated with blood pressure changes. Five machine learning methods were compared to develop the blood pressure variation identification model. Compared with the traditional blood pressure variation identification model with single characteristic based on single period area of PPG, the identification accuracy of mean blood pressure variation based on the proposed multi-feature random forest model in this paper was up to 90%, which was 17% higher than that of the traditional blood pressure variation identification model. By the proposed multi-dimensional features and the identification method, it is more accurate to detect the rapid variation in blood pressure and to adopt corresponding measures. Rapid and accurate identification of blood pressure variation under low blood volume ultimately has the potential to effectively avoid complications caused by abnormal blood pressure in patients with clinical bleeding trauma.
本研究的目的是识别血压变化，这在连续血压监测中很重要，尤其是在低血容量情况下，低血容量对生存至关重要。进行了一项初步研究，使用5头长白猪来识别低血容量时的血压变化。基于猪失血性休克的实验研究，提出了光电容积脉搏波描记法（PPG）新的多维形态特征，这些特征与血压变化密切相关。比较了五种机器学习方法来开发血压变化识别模型。与基于PPG单周期面积的传统单特征血压变化识别模型相比，本文提出的基于多特征随机森林模型的平均血压变化识别准确率高达90%，比传统血压变化识别模型高17%。通过所提出的多维特征和识别方法，检测血压的快速变化并采取相应措施更加准确。在低血容量情况下快速准确地识别血压变化最终有可能有效避免临床出血创伤患者因血压异常引起的并发症。
