School of Electrical Engineering and Computer Science, University of Ottawa, Ottawa, ON K1N 6N5, Canada.
School of Electrical Engineering and Computer Science, University of Ottawa, Ottawa, ON K1N 6N5, Canada.
Comput Methods Programs Biomed. 2017 Jul;145:1-10. doi: 10.1016/j.cmpb.2017.03.007. Epub 2017 Mar 9.
Accuracy in blood pressure (BP) estimation is essential for proper diagnosis and management of hypertension. Motion artifacts are considered external sources of inaccuracy and can be due to sudden arm motion, muscle tremor, shivering, and transport vehicle vibrations. In the proposed work, a new algorithmic stage is integrated in a non-invasive BP monitor. This stage suppresses the effect of the motion artifact and adjusts the pressure estimation before displaying it to users. The proposed stage is based on a 3-axis accelerometer signal, which helps in the accurate detection of the motion artifact. Both transient motion artifacts and artifact due to vibrations are suppressed using algorithms based on Empirical Mode Decomposition (EMD). Measurements with human subjects show that the proposed algorithms considerably improved the accuracy of the blood pressure estimates in comparison with the commonly-used conventional oscillometric algorithm that does not include an EMD-based stage for artifact suppression, and allowed the estimates to meet the requirements of the international ANSI/AAMI/ISO standard.
血压（BP）估计的准确性对于高血压的正确诊断和管理至关重要。运动伪影被认为是不准确的外部来源，可能是由于手臂突然运动、肌肉震颤、颤抖和运输车辆振动引起的。在提出的工作中，将一个新的算法阶段集成到非侵入式血压监测器中。该阶段可以抑制运动伪影的影响，并在将压力估计值显示给用户之前进行调整。所提出的阶段基于三轴加速度计信号，有助于准确检测运动伪影。使用基于经验模态分解（EMD）的算法可以抑制瞬态运动伪影和由于振动引起的伪影。与不包括基于 EMD 的伪影抑制阶段的常用传统示波法相比，人体测量结果表明，所提出的算法大大提高了血压估计的准确性，并且允许估计值满足国际 ANSI/AAMI/ISO 标准的要求。
