Elgendi Mohamed, Haugg Fridolin, Fletcher Richard Ribon, Allen John, Shin Hangsik, Alian Aymen, Menon Carlo
Biomedical and Mobile Health Technology Lab, Department of Health Sciences and Technology, ETH Zurich, Zurich, 8008, Switzerland.
Mechanical Engineering, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA.
Commun Med (Lond). 2024 Jul 12;4(1):140. doi: 10.1038/s43856-024-00555-2.
Photoplethysmography (PPG) is a non-invasive optical technique that measures changes in blood volume in the microvascular tissue bed of the body. While it shows potential as a clinical tool for blood pressure (BP) assessment and hypertension management, several sources of error can affect its performance. One such source is the PPG-based algorithm, which can lead to measurement bias and inaccuracy. Here, we review seven widely used measures to assess PPG-based algorithm performance and recommend implementing standardized error evaluation steps in their development. This standardization can reduce bias and improve the reliability and accuracy of PPG-based BP estimation, leading to better health outcomes for patients managing hypertension.
光电容积脉搏波描记法（PPG）是一种非侵入性光学技术，可测量身体微血管组织床中血容量的变化。虽然它作为一种用于血压（BP）评估和高血压管理的临床工具具有潜力，但有几个误差来源会影响其性能。其中一个来源是基于PPG的算法，它可能导致测量偏差和不准确。在此，我们回顾了七种广泛用于评估基于PPG算法性能的测量方法，并建议在其开发过程中实施标准化误差评估步骤。这种标准化可以减少偏差，提高基于PPG的血压估计的可靠性和准确性，从而为管理高血压的患者带来更好的健康结果。
