Discussion of Cuffless Blood Pressure Prediction Using Plethysmograph Based on a Longitudinal Experiment: Is the Individual Model Necessary?
Discussion of Cuffless Blood Pressure Prediction Using Plethysmograph Based on a Longitudinal Experiment: Is the Individual Model Necessary?
复制标题
DOI：
10.3390/life12010011
复制
发表时间：
2021-12-22
期刊：
影响因子：
--
通讯作者：
Kanaya S
中科院分区：
文献类型：
--
作者：
Kido K;Chen Z;Huang M;Tamura T;Chen W;Ono N;Takeuchi M;Altaf-Ul-Amin M;Kanaya S
利用体积描记器（PPG）信号来估计血压（BP）是有吸引力的，因为连续测量的方便性和可能性。然而，由于个人差异和数据的不足，小数据集的准确性和作为一个通用的方法的鲁棒性之间的困境仍然存在。为此，我们仔细研究了从特征选择到回归模型构建的整个流程，基于11名受试者为期一个月的实验。通过构造由五个不需要识别重搏切迹和舒张期峰值以及心率的一般PPG波形特征组成的解释性特征，构建了三个回归模型，即偏最小二乘、局部加权偏最小二乘和高斯过程模型，以反映关于拟合问题性质的基本假设。通过比较回归模型，可以确认单个高斯过程模型获得最佳结果，SBP和DBP的平均绝对误差为5.1 mmHg和4.6 mmHg，SBP和DBP的标准差为6.2 mmHg和5.4 mmHg。此外，个体模型的结果明显优于用所有受试者的数据建立的广义模型。
Using the Plethysmograph (PPG) signal to estimate blood pressure (BP) is attractive given the convenience and possibility of continuous measurement. However, due to the personal differences and the insufficiency of data, the dilemma between the accuracy for a small dataset and the robustness as a general method remains. To this end, we scrutinized the whole pipeline from the feature selection to regression model construction based on a one-month experiment with 11 subjects. By constructing the explanatory features consisting of five general PPG waveform features that do not require the identification of dicrotic notch and diastolic peak and the heart rate, three regression models, which are partial least square, local weighted partial least square, and Gaussian Process model, were built to reflect the underlying assumption about the nature of the fitting problem. By comparing the regression models, it can be confirmed that an individual Gaussian Process model attains the best results with 5.1 mmHg and 4.6 mmHg mean absolute error for SBP and DBP and 6.2 mmHg and 5.4 mmHg standard deviation for SBP and DBP. Moreover, the results of the individual models are significantly better than the generalized model built with the data of all subjects.
登录
查看更多内容
影响因子：
37.8
作者：
Makino, M;Hayashi, H;Ebihara, S
通讯作者：
Ebihara, S
影响因子：
3.9
作者：
Shin H;Min SD
通讯作者：
Min SD
影响因子：
4.6
作者：
Hayase, Toshiyuki
通讯作者：
Hayase, Toshiyuki
使用透皮光学成像技术进行基于智能手机的血压测量
影响因子：
7.5
作者：
Luo, Hong;Yang, Deye;Feng, Zhong-Ping
通讯作者：
Feng, Zhong-Ping
影响因子：
4.6
作者：
Xing, Xiaoman;Ma, Zhimin;Song, Mingxuan
通讯作者：
Song, Mingxuan