Open AccessPrediction of Continuous Blood Pressure Using Multiple Gated Recurrent Unit Embedded in SENet
Author(s) -
Xiaolei Chen,
Hao Chang,
Baoning Cao,
Yubing Lu,
Dongmei Lin
Publication year - 2022
Publication title -
journal of advanced computational intelligence and intelligent informatics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.172
H-Index - 20
eISSN - 1343-0130
pISSN - 1883-8014
DOI - 10.20965/jaciii.2022.p0256
Subject(s) - waveform , computer science , pulse (music) , blood pressure , channel (broadcasting) , artificial intelligence , pattern recognition (psychology) , algorithm , speech recognition , telecommunications , medicine , radar , detector
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.