Optimization and Evaluation of an Intelligent Short-Term Blood Glucose Prediction Model Based on Noninvasive Monitoring and Deep Learning Techniques

Abstract

Continuous noninvasive blood glucose monitoring and estimation management by using photoplethysmography (PPG) technology always have a series of problems, such as substantial time variability, inaccuracy, and complex nonlinearity. This paper proposes a blood glucose (BG) prediction model for more precise prediction based on BG series decomposition by complete aggregation empirical mode decomposition based on adaptive white noise (CEEMDAN) and the gated recurrent unit (GRU) that is optimized by improved bacterial foraging optimization (IBFO). Hierarchical clustering technology recombines the decomposed BG series according to their sample entropy and the correlations with the original BG trends. Dynamic BG trends are regressed separately for each recombined BG series by the GRU model to realize the more precise estimations, which are optimized by IBFO for its structure and superparameters. Through experiments, the optimized and basic LSTM, RNN, and support vector regression (SVR) are compared to evaluate the performance of the proposed model. The experimental results indicate that the root mean square error (RMSE) and mean absolute percentage error (MAPE) of the 15-min IBFO-GRU prediction is improved on average by about 13.1% and 18.4%, respectively, compared with those of the RNN and LSTM optimized by IBFO. Meanwhile, the proposed model improved the Clarke error grid results by about 2.6% and 5.0% compared with those of the IBFO-LSTM and IBFO-RNN in 30-min prediction and by 4.1% and 6.6% in 15-min ahead forecast, respectively. The evaluation outcomes of our proposed CEEMDAN-IBFO-GRU model have high accuracy and adaptability and can effectively provide early intervention control of the occurrence of hyperglycemic complications.

A method for portable PPG signal dynamic acquisition and denoise based on CEEMDAN and wavelet transform

“…The above-mentioned operations are repeated until there is no maximum or minmum value in the residue signal 𝑟 𝑛+1 (𝑡) [24]. The final decomposition level (𝑁) is 𝑛 + 1, and the final residue signal is 𝑟 𝑁 (𝑡).…”

“…EMD based methods are more elastic than WT and adaptive filtering, but for the discontinuous signal EMD decomposition can lead to modal aliasing. Aiming at this problem, some modified EMD methods are contributed by adding Gaussian white noise to the signal, such as ensemble EMD (EEMD) [21,22], complementary EEMD (CEEMD) [23] and complete EEMD with adaptive noise (CEEMDAN) [24,25]. EEMD and CEEMD directly add noise to the original signal, which can solve the problem of modal aliasing, but the added noise may affect the decomposed signal and produce the problem of noise residue, with a result of mutated reconstruction signal.…”

A Study of Optimization in Deep Neural Networks for Regression

“…Supplementing average value [16,17], linear interpolation [17,18], KNN [19,20] Dimensionality reduction PCA [8], pooling layer [21], t-SNE [22], SPCA [23] Removing outliers Pauta criterion [18], EWMA [24] Feature selection PSO [1], LASSO [8,25], ASO [26], GA [27], MI [28], GRA [29], PCC [30], CCA [31] Decomposition EMD [32], EEMD [33], CEEMDAN [19,27,[34][35][36][37][38], ICEEMDAN [39], SSA [40,41], VMD [42], SVMD [43] Normalization [6,17,20,27,30,31,34,42,[44][45][46][47][48][49]…”

Retracted: Optimization and Evaluation of an Intelligent Short‐Term Blood Glucose Prediction Model Based on Noninvasive Monitoring and Deep Learning Techniques

“…This article has been retracted by Hindawi following an investigation undertaken by the publisher [ 1 ]. This investigation has uncovered evidence of one or more of the following indicators of systematic manipulation of the publication process: Discrepancies in scope Discrepancies in the description of the research reported Discrepancies between the availability of data and the research described Inappropriate citations Incoherent, meaningless and/or irrelevant content included in the article Peer-review manipulation …”

“…Tis article has been retracted by Hindawi following an investigation undertaken by the publisher [1]. Tis investigation has uncovered evidence of one or more of the following indicators of systematic manipulation of the publication process:…”