School of Biomedical Engineering (Suzhou), Division of Life Sciences and Medicine, University of Sciences and Technology of China, Suzhou, Jiangsu, People's Republic of China.
Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, Jiangsu, People's Republic of China.
Physiol Meas. 2021 Nov 2;42(10). doi: 10.1088/1361-6579/ac2a71.
. In this study, we aimed to estimate blood pressure (BP) from in-ear photoplethysmography (PPG). This novel implementation provided an unobtrusive and steady way of recording PPG, whereas previous PPG measurements were mostly performed at the wrist, finger, or earlobe.. The time between forward and reflected PPG waves was very short at the ear site. To minimize errors introduced by feature extraction, a multi-Gaussian decomposition of in-ear PPG was performed. Both hand-crafted and whole-based features were extracted and the best combination of features was selected using a backward-search wrapper method and evaluated by the Akaike information criteria. Hemodynamic parameters such as compliance and inertance were estimated from a four-element Windkessel (WK4) model, which was used to pre-classify PPG signals and generate different BP estimation algorithms. Calibration was done by using previous measurements from the same class. To validate this novel approach, 53 subjects were recruited for a one-month follow-up study, and 17 subjects were recruited for a two-month follow-up study. Calibrated systolic BP estimation accuracy was significantly improved with inertance-based pre-classification, while diastolic BP showed less improvement.. With proper feature selection, pre-classification and calibration, we have achieved a mean absolute error of 5.35 mmHg for SBP estimation, compared to 6.16 mmHg if no pre-classification was carried out. The performance did not deteriorate in two months, showing a decent BP trend-tracking ability.. The study demonstrated the feasibility of in-ear PPG to reliably measure BP, which represents an important technological advancement in terms of unobtrusiveness and steadiness.
. 在这项研究中，我们旨在通过耳内光体积描记法（PPG）来估计血压（BP）。这种新颖的实现方式提供了一种非侵入性且稳定的 PPG 记录方式，而之前的 PPG 测量大多是在手腕、手指或耳垂处进行的。. 耳内 PPG 的正向和反向波之间的时间间隔非常短。为了最小化特征提取引入的误差，我们对耳内 PPG 进行了多高斯分解。提取了手工制作和整体特征，并使用后向搜索包装方法选择了最佳特征组合，并通过 Akaike 信息准则进行了评估。从四元素 Windkessel（WK4）模型中估计了顺应性和惯性等血液动力学参数，该模型用于对 PPG 信号进行预分类，并生成不同的 BP 估计算法。通过使用同一类别的先前测量值进行校准。为了验证这种新方法，我们招募了 53 名受试者进行为期一个月的随访研究，招募了 17 名受试者进行为期两个月的随访研究。基于惯性的预分类显著提高了校准后的收缩压估计准确性，而舒张压的改善较小。. 通过适当的特征选择、预分类和校准，我们实现了收缩压估计的平均绝对误差为 5.35mmHg，而如果不进行预分类，则为 6.16mmHg。在两个月内，性能没有恶化，显示出良好的 BP 趋势跟踪能力。. 该研究证明了耳内 PPG 可靠测量 BP 的可行性，这代表了非侵入性和稳定性方面的重要技术进步。
