基于特征的视频数据非接触式血压估计方法

Carolin Wuerich, Eva-Maria Humm, C. Wiede, Gregor Schiele
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引用次数: 2

摘要

传统的血压监测器和传感器在准确性、测量时间、舒适性或安全性方面存在一些限制。为了解决这些限制,我们实现并测试了一种基于代理的非接触式血压估计方法,该方法依赖于相机捕获的单个远程光电容积图(rPPG)。从该rPPG信号中,我们计算了120个特征,并进行了顺序前向特征选择以获得最佳特征子集。通过多层感知器模型,我们得到收缩压的平均绝对误差±标准差为5.50美元\pm 4.52美元mmHg,舒张压的平均绝对误差为3.73美元\pm 2.86美元mmHg。与之前的研究相反,我们的模型是在包括血压正常值、高血压前期值和高血压值的数据集上进行训练和测试的。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A Feature-based Approach on Contact-less Blood Pressure Estimation from Video Data
Conventional blood pressure monitors and sensors have several limitations in terms of accuracy, measurement time, comfort or safety. To address these limitations, we realized and tested a surrogate-based contact-less blood pressure estimation method which relies on a single remote photoplethysmogram (rPPG) captured by camera. From this rPPG signal, we compute 120 features, and perform a sequential forward feature selection to obtain the best subset of features. With a multilayer perceptron model, we obtain a mean absolute error ± standard deviation of MAE $5.50\pm 4.52$ mmHg for systolic pressure and $3.73\pm 2.86$ mmHg for diastolic pressure. In contrast to previous studies, our model is trained and tested on a data set including normotensive, pre-hypertensive and hypertensive values.
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