Noninvasive Cuffl ess Blood Pressure Monitoring. How Mechanism-Driven and Data-Driven Models Can Help in Clinical Practice

Continuous noninvasive cuffl ess blood pressure (BP) monitoring is essential for early detection and treatment of hypertension. In this paper, we provide an overview of the recent advancements in cuffl ess BP sensors. These include contact wearable sensors such as electrocardiography (ECG), photoplethysmography (PPG), contact non-wearable sensors such as ballistocardiography (BCG), and contactless sensors such as video plethysmography (VPG). These sensors employ diff erent measuring mechanisms such as pulse arrival time (PAT), pulse transit time (PTT), and pulse wave analysis (PWA) to estimate BP. However, challenges exist in the eff ective use and interpretation of signal features to obtain clinically reliable BP measurements. The correlations between signal features and BP are obtained by mechanism-driven models which use physiological principles to identify mathematical correlations, and data-driven models which use machine learning algorithms to analyze observational data to identify multidimensional correlations. On the one hand, applying mechanism-driven models to non-linear scenarios and incomplete or noisy data is challenging On the other hand, data-driven models require a large amount of data in order to prevent physically inconsistent predictions, resulting in poor generalization. From this perspective, this paper proposes to combine the strengths of mechanism-driven and data-driven approaches to obtain a more comprehensive approach, the physiology-informed machine-learning approach, with the goal of enhancing the accuracy, interpretability, and scalability of continuous cuffl ess BP monitoring. This holds promise for personalized clinical applications and the advancement of hypertension management.

连续无创无袖带血压（BP）监测对于高血压的早期发现和治疗至关重要。在本文中，我们概述了无袖带血压传感器的最新进展。这些包括接触式可穿戴传感器，如心电图（ECG）、光电容积脉搏波描记法（PPG），接触式非穿戴传感器，如心冲击图（BCG），以及非接触式传感器，如视频容积脉搏波描记法（VPG）。这些传感器采用不同的测量机制，如脉搏波到达时间（PAT）、脉搏波传播时间（PTT）和脉搏波分析（PWA）来估算血压。然而，在有效利用和解读信号特征以获得临床可靠的血压测量值方面存在挑战。信号特征与血压之间的相关性通过机制驱动模型和数据驱动模型获得，机制驱动模型利用生理原理来确定数学相关性，数据驱动模型利用机器学习算法分析观测数据以确定多维相关性。一方面，将机制驱动模型应用于非线性场景以及不完整或有噪声的数据具有挑战性；另一方面，数据驱动模型需要大量数据以防止物理上不一致的预测，从而导致泛化能力差。从这个角度来看，本文提出结合机制驱动和数据驱动方法的优势，以获得一种更全面的方法，即基于生理信息的机器学习方法，目的是提高连续无袖带血压监测的准确性、可解释性和可扩展性。这为个性化临床应用和高血压管理的进步带来了希望。