Lee Soojeong, Rajan Sreeraman, Jeon Gwanggil, Chang Joon-Hyuk, Dajani Hilmi R, Groza Voicu Z
Department of Electronic Engineering, Hanyang University 222 Wangsimni-ro, Seongdong, Seoul 133-791, South Korea.
Department of Systems and Computer Engineering, Carleton University, Ottawa, Ontario, Canada, K1S 5B6.
Comput Biol Med. 2017 Jun 1;85:112-124. doi: 10.1016/j.compbiomed.2015.11.008. Epub 2015 Nov 22.
Blood pressure (BP) is one of the most important vital indicators and plays a key role in determining the cardiovascular activity of patients.
This paper proposes a hybrid approach consisting of nonparametric bootstrap (NPB) and machine learning techniques to obtain the characteristic ratios (CR) used in the blood pressure estimation algorithm to improve the accuracy of systolic blood pressure (SBP) and diastolic blood pressure (DBP) estimates and obtain confidence intervals (CI). The NPB technique is used to circumvent the requirement for large sample set for obtaining the CI. A mixture of Gaussian densities is assumed for the CRs and Gaussian mixture model (GMM) is chosen to estimate the SBP and DBP ratios. The K-means clustering technique is used to obtain the mixture order of the Gaussian densities.
The proposed approach achieves grade "A" under British Society of Hypertension testing protocol and is superior to the conventional approach based on maximum amplitude algorithm (MAA) that uses fixed CR ratios. The proposed approach also yields a lower mean error (ME) and the standard deviation of the error (SDE) in the estimates when compared to the conventional MAA method. In addition, CIs obtained through the proposed hybrid approach are also narrower with a lower SDE.
The proposed approach combining the NPB technique with the GMM provides a methodology to derive individualized characteristic ratio. The results exhibit that the proposed approach enhances the accuracy of SBP and DBP estimation and provides narrower confidence intervals for the estimates.
血压（BP）是最重要的生命体征之一，在确定患者心血管活动中起关键作用。
本文提出一种由非参数自助法（NPB）和机器学习技术组成的混合方法，以获取血压估计算法中使用的特征比率（CR），从而提高收缩压（SBP）和舒张压（DBP）估计的准确性并获得置信区间（CI）。NPB技术用于规避获取CI对大样本集的需求。假设CR服从高斯密度混合分布，并选择高斯混合模型（GMM）来估计SBP和DBP比率。使用K均值聚类技术来获得高斯密度的混合阶数。
所提出的方法在英国高血压学会测试协议下达到“A”级，优于基于使用固定CR比率的最大幅度算法（MAA）的传统方法。与传统的MAA方法相比，所提出的方法在估计中还产生了更低的平均误差（ME）和误差标准差（SDE）。此外，通过所提出的混合方法获得的CI也更窄，SDE更低。
所提出的将NPB技术与GMM相结合的方法提供了一种推导个性化特征比率的方法。结果表明，所提出的方法提高了SBP和DBP估计的准确性，并为估计提供了更窄的置信区间。
