现代电子技术2024，Vol.47Issue(18)：83-88,6.DOI:10.16652/j.issn.1004-373x.2024.18.014
基于RIME和1DCNN-LSTM-Attention的无创血糖预测模型研究
Research on non-invasive blood glucose prediction model based on RIME and 1DCNN-LSTM-Attention
贺义博 1靳鸿 1周春 2屈盛玉1
作者信息
- 1. 中北大学 电子测试技术国家重点实验室,山西 太原 030051
- 2. 中国船舶集团有限公司第七〇五研究所,陕西 西安 710075
- 折叠
摘要
Abstract
The realization of non-invasive blood glucose detection is of great significance for diabetes patients.However,the current non-invasive blood glucose meters on the market have the problem of low detect ion accuracy.In order to improve the accuracy of non-invasive blood glucose detection,a 1DCNN-LSTM-Attention hybrid prediction model is constructed based on a near-infrared non-invasive blood glucose detector,and the rime ice optimization algorithm(RIME)is introduced.The model can extract local features from the data by means of 1-dimensional convolutional neural network(1DCNN),and the extracted feature vectors are used as the inputs of the long short term memory(LSTM)network,to capture dependencies in the data.The attention mechanism(Attention)can be used to assign different weights to the output of LSTM to enhance key information extraction.The RIME algorithm is used to optimize model parameters so as to avoid getting stuck in local optima.The results show that the prediction effect of the 1DCNN-LSTM Attention mixed model with RIME is better than that of the 1DCNN-LSTM single model and the 1DCNN-LSTM and 1DCNN-LSTM Attention models.The mean absolute error between the predicted blood glucose value and the invasive blood glucose value is 0.121 0,the mean square error is 0.018 6,and the correlation coefficient can reach 0.982 3.The model is helpful for improving the accuracy and reliability of near-infrared non-invasive blood glucose detection.关键词
近红外无创血糖检测/一维卷积神经网络/霜冰优化算法/长短期记忆网络/注意力机制/参数优化Key words
near infrared non-invasive blood glucose detection/one-dimensional convolutional neural network/rime ice optimization algorithm/long short-term memory network/attention mechanism/parameter optimization分类
信息技术与安全科学引用本文复制引用
贺义博,靳鸿,周春,屈盛玉..基于RIME和1DCNN-LSTM-Attention的无创血糖预测模型研究[J].现代电子技术,2024,47(18):83-88,6.
