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JACIII Vol.26 No.2 pp. 256-263
doi: 10.20965/jaciii.2022.p0256
(2022)

Paper:

Prediction of Continuous Blood Pressure Using Multiple Gated Recurrent Unit Embedded in SENet

Xiaolei Chen*,†, Hao Chang*, Baoning Cao*, Yubing Lu**, and Dongmei Lin***

*College of Electrical and Information Engineering, Lanzhou University of Technology
No.287 Langongping Road, Qilihe District, Lanzhou 730050, China

**Key Laboratory of Gansu Advanced Control for Industrial Processes, Lanzhou University of Technology
No.287 Langongping Road, Qilihe District, Lanzhou 730050, China

***National Demonstration Center for Experimental Electrical and Control Engineering Education, Lanzhou University of Technology
No.287 Langongping Road, Qilihe District, Lanzhou 730050, China

Corresponding author

Received:
October 21, 2019
Accepted:
February 14, 2022
Published:
March 20, 2022
Keywords:
gated recurrent unit, SENet, blood pressure prediction, pulse information
Abstract
Prediction of Continuous Blood Pressure Using Multiple Gated Recurrent Unit Embedded in SENet

Prediction of continuous blood pressure using multiple gated recurrent unit embedded in SENet

In order to accurately predict blood pressure waveform from pulse waveform, a multiple gated recurrent unit (GRU) model embedded in squeeze-and-excitation network (SENet) is proposed for continuous blood pressure prediction. Firstly, the features of the pulse are extracted from multiple GRU channels. Then, the SENet module is embedded to learn the interdependence among the channels, so as to get the weight of each channel. Finally, the weights were added to each channel and the predicted continuous blood pressure values were obtained by integrating the two linear layers. The experimental results show that the embedded SENet can effectively enhance the predictive ability of multi-GRU structure and obtain good continuous blood pressure waveform. Compared with the LSTM and GRU model without SENet, the MSE errors of the proposed method are reduced by 29.3% and 25.0% respectively, the training time of the proposed method are decreased by 69.8% and 68.7%, the test time is reduced by 65.9% and 25.2% and it has the fewest model parameters.

Cite this article as:
Xiaolei Chen, Hao Chang, Baoning Cao, Yubing Lu, and Dongmei Lin, “Prediction of Continuous Blood Pressure Using Multiple Gated Recurrent Unit Embedded in SENet,” J. Adv. Comput. Intell. Intell. Inform., Vol.26, No.2, pp. 256-263, 2022.
Data files:
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Last updated on May. 20, 2022