Feature-Based Neural Network Approach for Oscillometric Blood Pressure Estimation
Feature-Based Neural Network Approach for Oscillometric Blood Pressure Estimation
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DOI：
10.1109/tim.2011.2123210
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发表时间：
2011-08-01
影响因子：
5.6
通讯作者：
Rajan, Sreeraman
中科院分区：
文献类型：
--
作者：
Forouzanfar, Mohamad;Dajani, Hilmi R.;Rajan, Sreeraman
在本文中，我们提出了一种新的基于特征的神经网络（NN）方法，用于从手腕振荡测量中估计血压（BP）。与以前使用原始振荡波形包络（OMWE）作为神经网络输入的方法不同，在本文中，我们建议使用从包络中提取的特征。OMWE在数学上被建模为两个高斯函数的和。利用Levenberg-Marquardt算法对模型与OMWE之间的最小二乘误差进行最小化，得到该模型的最优参数，并将其作为特征。然后设计两个单独的前馈神经网络（ffnn）来利用这些特征估计收缩期和舒张期bp。ffnn使用弹性反向传播学习算法进行训练，并在85名受试者记录的BP测量数据集上进行测试。然后将其性能与传统的最大振幅算法、自适应神经模糊推理系统和已经发表的基于神经网络的方法进行比较。结果表明，该方法在BP估计中获得了较低的平均绝对误差和误差标准差。此外，该方法还具有相对于设计参数的复杂度较低、训练数据集较小、计算量较小等优点。
In this paper, we present a novel feature-based neural network (NN) approach for estimation of blood pressure (BP) from wrist oscillometric measurements. Unlike previous methods that use the raw oscillometric waveform envelope (OMWE) as input to the NN, in this paper, we propose to use features extracted from the envelope. The OMWE is mathematically modeled as a sum of two Gaussian functions. The optimum parameters of this model are found by minimizing the least squares error between the model and the OMWE using the Levenberg-Marquardt algorithm and are used as features. Two separate feed-forward NNs (FFNNs) are then designed to estimate the systolic and diastolic BPs using these features. The FFNNs are trained using the resilient backpropagation learning algorithm and tested on a data set of BP measurements recorded from 85 subjects. The performance is then compared with that of the conventional maximum amplitude algorithm, adaptive neuro-fuzzy inference system, and already published NN-based methods. It is found that the proposed approach achieves lower values of mean absolute error and standard deviation of error in the estimation of BP. In addition, the proposed approach has the following advantages: lower complexity with respect to the design parameters, smaller training data set, and lower computational load.