Computer Engineering and Applications ›› 2021, Vol. 57 ›› Issue (7): 130-135.DOI: 10.3778/j.issn.1002-8331.2001-0083

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Continuous Blood Pressure Prediction Based on Improved SENet Convolutional Neural Network

CHANG Hao, CHEN Xiaolei, ZHANG Aihua, LI Ce, LIN Dongmei   

  1. 1.College of Electrical and Information Engineering, Lanzhou University of Technology, Lanzhou 730050, China
    2.Key Laboratory of Gansu Advanced Control for Industrial Processes, Lanzhou University of Technology, Lanzhou 730050, China
    3.National Demonstration Center for Experimental Electrical and Control Engineering Education, Lanzhou University of Technology, Lanzhou 730050, China
  • Online:2021-04-01 Published:2021-04-02



  1. 1.兰州理工大学 电气工程与信息工程学院,兰州 730050
    2.兰州理工大学 甘肃省工业过程先进控制重点实验室,兰州 730050
    3.兰州理工大学 电气与控制工程国家级实验教学示范中心,兰州 730050


This paper proposes a continuous blood pressure prediction method based on improved SENet convolutional neural network and a self-learning parameter filter. The experimental results show that the improved SENet can effectively increase the prediction ability of simple convolutional neural networks for time series data. When the number of convolutional layers is two, three and four layers, the prediction accuracy is improved by 34.8%, 23.5 % and 36.0%. On this basis, the self-learning parameter filter is used to eliminate the burr in the predicted blood pressure waveform, and finally a smooth continuous blood pressure prediction result is obtained.

Key words: convolutional neural network, Sequeeze-and-Excitation Network(SENet), blood pressure prediction, pulse information



关键词: 卷积神经网络, SENet, 血压预测, 脉搏信息