Glucose trend prediction model based on improved wavelet transform and gated recurrent unit

Tao Yang; Qicheng Yang; Yibo Zhou; Chuanbiao Wen; Tao Yang; Qicheng Yang; Yibo Zhou; Chuanbiao Wen

doi:10.3934/mbe.2023760

Mathematical Biosciences and Engineering

2023, Volume 20, Issue 9: 17037-17056. doi: 10.3934/mbe.2023760

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Glucose trend prediction model based on improved wavelet transform and gated recurrent unit

1.
College of Intelligent Medicine, Chengdu University of Traditional Chinese Medicine, Chengdu 610000, Sichuan, China
2.
Xin-Huangpu Joint Innovation Institute of Chinese Medicine, Guangzhou 510000, Guangdong, China
3.
Beijing Certificate Authority Co., Ltd., Beijing 100000, China

Academic Editor: Giuseppe Ciaburro

Received: 30 May 2023 Revised: 07 August 2023 Accepted: 14 August 2023 Published: 29 August 2023

Glucose trend prediction based on continuous glucose monitoring (CGM) data is a crucial step in the implementation of an artificial pancreas (AP). A glucose trend prediction model with high accuracy in real-time can greatly improve the glycemic control effect of the artificial pancreas and effectively prevent the occurrence of hyperglycemia and hypoglycemia. In this paper, we propose an improved wavelet transform threshold denoising algorithm for the non-linearity and non-smoothness of the original CGM data. By quantitatively comparing the mean square error (MSE) and signal-to-noise ratio (SNR) before and after the improvement, we prove that the improved wavelet transform threshold denoising algorithm can reduce the degree of distortion after the smoothing of CGM data and improve the extraction effect of CGM data features at the same time. Based on this finding, we propose a glucose trend prediction model (IWT-GRU) based on the improved wavelet transform threshold denoising algorithm and gated recurrent unit. We compared the root mean square error (RMSE), mean absolute percentage error (MAPE), and coefficient of determination ($ {\mathrm{R}}^{2} $) of Recurrent Neural Networks (RNN), Long Short-Term Memory (LSTM), Support vector regression (SVR), Gated Recurrent Unit (GRU) and IWT-GRU on the original CGM monitoring data of 80 patients for 7 consecutive days with different prediction horizon (PH). The results showed that the IWT-GRU model outperformed the other four models. At PH = 45 min, the RMSE was 0.5537 mmol/L, MAPE was 2.2147%, $ {\mathrm{R}}^{2} $ was 0.989 and the average runtime was only 37.2 seconds. Finally, we analyze the limitations of this study and provide an outlook on the future direction of blood glucose trend prediction.
- blood glucose trend prediction,
- improved wavelet transform,
- gated recurrent unit,
- continuous glucose monitoring
Citation: Tao Yang, Qicheng Yang, Yibo Zhou, Chuanbiao Wen. Glucose trend prediction model based on improved wavelet transform and gated recurrent unit[J]. Mathematical Biosciences and Engineering, 2023, 20(9): 17037-17056. doi: 10.3934/mbe.2023760

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Abstract

Glucose trend prediction based on continuous glucose monitoring (CGM) data is a crucial step in the implementation of an artificial pancreas (AP). A glucose trend prediction model with high accuracy in real-time can greatly improve the glycemic control effect of the artificial pancreas and effectively prevent the occurrence of hyperglycemia and hypoglycemia. In this paper, we propose an improved wavelet transform threshold denoising algorithm for the non-linearity and non-smoothness of the original CGM data. By quantitatively comparing the mean square error (MSE) and signal-to-noise ratio (SNR) before and after the improvement, we prove that the improved wavelet transform threshold denoising algorithm can reduce the degree of distortion after the smoothing of CGM data and improve the extraction effect of CGM data features at the same time. Based on this finding, we propose a glucose trend prediction model (IWT-GRU) based on the improved wavelet transform threshold denoising algorithm and gated recurrent unit. We compared the root mean square error (RMSE), mean absolute percentage error (MAPE), and coefficient of determination ($ {\mathrm{R}}^{2} $) of Recurrent Neural Networks (RNN), Long Short-Term Memory (LSTM), Support vector regression (SVR), Gated Recurrent Unit (GRU) and IWT-GRU on the original CGM monitoring data of 80 patients for 7 consecutive days with different prediction horizon (PH). The results showed that the IWT-GRU model outperformed the other four models. At PH = 45 min, the RMSE was 0.5537 mmol/L, MAPE was 2.2147%, $ {\mathrm{R}}^{2} $ was 0.989 and the average runtime was only 37.2 seconds. Finally, we analyze the limitations of this study and provide an outlook on the future direction of blood glucose trend prediction.

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