Short-term Prediction of Blood Glucose Based on CEEMDAN and ELM
Wang Yannian1, Guo Zhanli1, Yuan Jinlei2, Li Quanzhong3*
1School of Information Engineering, Zhengzhou University, Zhengzhou 450001, China 2The Fifth Affiliated Hospital, Zhengzhou University, Zhengzhou 450052, China 3Department of Endocrinology, Henan People’s Hospital, Zhengzhou 450003, China
Abstract A short-term prediction model of blood glucose based on the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and extreme learning machine (ELM) was proposed to improve the forecasting accuracy of blood glucose, which is timing-varying, nonlinear and non-stationary in diabetics. Firstly, by means of CEEMDAN, time sequence of blood glucose was decomposed into several intrinsic mode function (IMF) with different frequencies and a residual to reduce the non-stationary. Next, ELMs were built for each IMF and residual component to improve the forecasting accuracy, and the all forecasts of ELMs were fused to produce the prediction of blood glucose. Finally, an early alarm algorithm of hypoglycemia was proposed based on the short-term prediction model. The model was verified by 56 cases of diabetic in Department of Endocrinology of Henan Province People's Hospital. The experimental results showed that comparing to ELM model and EMD-ELM model, the proposed prediction model of blood glucose based on CEEMDAN-ELM could achieve 45 min prediction in advance, the RMSE was 0.2051 and the MAPE was 2.1164%. The false alarm rate and missing alarm rate of early alarm algorithm of hypoglycemia were 0.97% and 7.55% respectively. The 45 min prediction ahead provided sufficient time for doctors and diabetics to control the blood glucose concentration, especially for diabetics with hypoglycemia.
Wang Yannian,Guo Zhanli,Yuan Jinlei, et al. Short-term Prediction of Blood Glucose Based on CEEMDAN and ELM[J]. Chinese Journal of Biomedical Engineering, 2017, 36(6): 702-710.
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