基于深度循环网络结合上下文信息的血压预测  

Blood pressure prediction with deep recurrent network and contextual information

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作  者:孙永莹 郑茜颖[1] SUN Yongying;ZHENG Qianying(College of Physics&Information Engineering,Fuzhou University,Fuzhou 350108,China)

机构地区:[1]福州大学物理与信息工程学院,福建福州350108

出  处:《微电子学与计算机》2023年第7期10-17,共8页Microelectronics & Computer

基  金:福建省科技重点产业引导项目(2020H0007)。

摘  要:近年来,高血压患者的比例不断上升,如何在血压值异常前发出警报提早治疗成为广泛关注的研究课题.为解决这一问题,提出了一种基于多层长短期记忆网络(LSTM)结合上下文信息层的组合预测模型.使用双向LSTM结构添加了从负时间方向的时序信息对当前状态的影响,加入LSTM残差连接解决多层网络带来的梯度消失和梯度爆炸问题.在输出层之前增加了一个额外的加入了用户的基本信息数据的全连接层.额外层的激活函数为修正线性单元(ReLU),使用多个时序数据对不同时段的血压进行预测.实验结果表明使用24个时序的实验结果最佳.在24个时序的数据集上,进行不同时段1 h、6 h、12 h、24 h的血压预测,预测误差和预测偏差对于收缩压分别为0.002644、0.003952、0.004216、0.005528和0.037796、0.047931、0.049879、0.057454,对于舒张压分别为0.001226、0.001554、0.001706、0.001955和0.024293、0.028369、0.030190、0.032668,实验误差与其他模型相比,所提模型预测误差和预测偏差都得到降低.In recent years,the proportion of patients with hypertension has been increasing.How to give an alarm before abnormal blood pressure value and early treatment has become a widely concerned research topic.The structure of bidirectional LSTM is used to add the influence of the timing information from the negative time direction on the current state,and LSTM with residual connection is added to solve the gradient disappearance and gradient explosion caused by multi-layer network.Before the output layer,an additional layer is added to add the basic information data of the user and the activation function of the extra layer is modified linear unit(ReLU),multiple time series data are used to predict the blood pressure of different periods.The experimental results show that the best results using 24 time series data sets.On the dataset of 24 time series,blood pressure prediction of 1 h,6 h,12 h and 24 h at different time periods was performed.The prediction error and the prediction deviation for systolic blood pressure are respectively 0.002644,0.003952,0.004216,0.005528 and 0.037796,0.047931,0.049879,0.057454,for diastolic blood pressure are respectively 0.001226,0.001554,0.001706,0.001955 and 0.024293,0.028369,0.030190,0.032668.Experimental error compared with other models,the prediction error and prediction bias of the proposed model are reduced.

关 键 词:血压预测 上下文信息 深度循环网络 时间序列 

分 类 号:TP389.1[自动化与计算机技术—计算机系统结构]

 

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