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Oscillometric Blood Pressure Estimation Based on Deep Learning

机译：基于深度学习的示波法血压估计

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摘要

Oscillometric measurement is widely used to estimate systolic blood pressure (SBP) and diastolic blood pressure (DBP). In this paper, we propose a deep belief network (DBN)-deep neural network (DNN) to learn about the complex nonlinear relationship between the artificial feature vectors obtained from the oscillometric wave and the reference nurse blood pressures using the DBN-DNN-based-regression model. Our DBN-DNN is a powerful generative network for feature extraction and can address to stick in local minima through a special pretraining phase. Therefore, this model provides an alternative way for replacing a popular shallow model. Based on this, we apply the DBN-DNN-based regression model to estimate the SBP and DBP. However, there are a small amount of data samples, which is not enough to train the DBN-DNN without the overfitting problem. For this reason, we use the parametric bootstrap-based artificial features, which are used as training samples to efficiently learn the complex nonlinear functions between the feature vectors obtained and the reference nurse blood pressures. As far as we know, this is one of the first studies using the DBN-DNN-based regression model for BP estimation when a small training sample is available. Our DBN-DNN-based regression model provides a lower standard deviation of error, mean error, and mean absolute error for the SBP and DBP as compared with the conventional methods.
机译：示波法测量广泛用于估计收缩压（SBP）和舒张压（DBP）。在本文中，我们提出了一种深层信念网络（DBN）-深层神经网络（DNN），以了解使用基于DBN-DNN的示波法获得的人工特征向量与参考护士血压之间的复杂非线性关系回归模型。我们的DBN-DNN是强大的生成网络，用于特征提取，并且可以通过特殊的预训练阶段解决将局部最小值保持不变的问题。因此，该模型提供了替代流行的浅层模型的替代方法。基于此，我们应用基于DBN-DNN的回归模型来估计SBP和DBP。但是，只有少量数据样本，这不足以在没有过度拟合问题的情况下训练DBN-DNN。因此，我们使用基于参数引导程序的人工特征作为训练样本，以有效地学习获得的特征向量与参考护士血压之间的复杂非线性函数。据我们所知，这是在有少量训练样本时使用基于DBN-DNN的回归模型进行BP估计的首批研究之一。与传统方法相比，我们基于DBN-DNN的回归模型为SBP和DBP提供了更低的误差，平均误差和平均绝对误差的标准偏差。

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