Owing to the recent advancements in wearable devices for health care, the importance of BP estimation without cuffs increases. Cuff technologies are inappropriate for continuous BP measurement due to their inconvenient usage, invasive character, necessity of calibration, large size, and inability to perform long-term monitoring. Normally, the algorithm used for cuffless BP prediction employs machine learning models that operate according to the data-driven approach. However, although they show high numerical accuracy, ML models do not provide any interpretability, resulting in poor physiological validity and clinical applicability. We propose a combination of Windkessel and ML models that incorporates the physical aspects into the latter one. It is performed by reformulating Windkessel into a form that will allow employing ML models. The result is a system of ODEs which can be used in the neural network. Thus, the inclusion of physical constraints improves the data-driven approach by making models consistent with physics, understandable, and robust. For illustration, we apply the described technique using a publicly available MIMIC-II database that we obtain from the UCI Machine Learning Repository.

翻译：随着可穿戴健康设备的快速发展，无袖带血压估计的重要性日益凸显。袖带技术因使用不便、侵入性强、需校准、体积庞大且无法长期监测等缺陷，不适用于连续血压测量。当前无袖带血压预测算法多采用基于数据驱动方法的机器学习模型。尽管这类模型具有较高的数值精度，却缺乏可解释性，导致生理有效性和临床适用性不足。本文提出将Windkessel模型与机器学习模型相结合，通过将物理机制融入机器学习模型。具体实现方式是将Windkessel方程重构为适用于机器学习模型的形式，最终形成可嵌入神经网络求解的常微分方程组系统。通过引入物理约束，该方案在保持模型与物理规律一致性的同时，增强了数据驱动方法的可解释性和鲁棒性。为验证方法有效性，我们采用UCI机器学习库公开的MIMIC-II数据库进行实验分析。