Glucose trend prediction model based on improved wavelet transform and gated recurrent unit
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 effective...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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AIMS Press
2023-08-01
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| Series: | Mathematical Biosciences and Engineering |
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| Online Access: | https://www.aimspress.com/article/doi/10.3934/mbe.2023760?viewType=HTML |
| _version_ | 1797672127559630848 |
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| author | Tao Yang Qicheng Yang Yibo Zhou Chuanbiao Wen |
| author_facet | Tao Yang Qicheng Yang Yibo Zhou Chuanbiao Wen |
| author_sort | Tao Yang |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-11T21:25:38Z |
| format | Article |
| id | doaj.art-20eb11a9e46443e3b381ce06018f386e |
| institution | Directory Open Access Journal |
| issn | 1551-0018 |
| language | English |
| last_indexed | 2024-03-11T21:25:38Z |
| publishDate | 2023-08-01 |
| publisher | AIMS Press |
| record_format | Article |
| series | Mathematical Biosciences and Engineering |
| spelling | doaj.art-20eb11a9e46443e3b381ce06018f386e2023-09-28T01:29:11ZengAIMS PressMathematical Biosciences and Engineering1551-00182023-08-01209170371705610.3934/mbe.2023760Glucose trend prediction model based on improved wavelet transform and gated recurrent unitTao Yang 0Qicheng Yang 1Yibo Zhou2Chuanbiao Wen31. 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, China1. 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, China3. Beijing Certificate Authority Co., Ltd., Beijing 100000, China2. Xin-Huangpu Joint Innovation Institute of Chinese Medicine, Guangzhou 510000, Guangdong, ChinaGlucose 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.https://www.aimspress.com/article/doi/10.3934/mbe.2023760?viewType=HTMLblood glucose trend predictionimproved wavelet transformgated recurrent unitcontinuous glucose monitoring |
| spellingShingle | Tao Yang Qicheng Yang Yibo Zhou Chuanbiao Wen Glucose trend prediction model based on improved wavelet transform and gated recurrent unit Mathematical Biosciences and Engineering blood glucose trend prediction improved wavelet transform gated recurrent unit continuous glucose monitoring |
| title | Glucose trend prediction model based on improved wavelet transform and gated recurrent unit |
| title_full | Glucose trend prediction model based on improved wavelet transform and gated recurrent unit |
| title_fullStr | Glucose trend prediction model based on improved wavelet transform and gated recurrent unit |
| title_full_unstemmed | Glucose trend prediction model based on improved wavelet transform and gated recurrent unit |
| title_short | Glucose trend prediction model based on improved wavelet transform and gated recurrent unit |
| title_sort | glucose trend prediction model based on improved wavelet transform and gated recurrent unit |
| topic | blood glucose trend prediction improved wavelet transform gated recurrent unit continuous glucose monitoring |
| url | https://www.aimspress.com/article/doi/10.3934/mbe.2023760?viewType=HTML |
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