Long-Term Prediction of Blood Glucose Levels in Type 1 Diabetes Using a CNN-LSTM-Based Deep Neural Network

Abstract

Background: In this work, we leverage state-of-the-art deep learning-based algorithms for blood glucose (BG) forecasting in people with type 1 diabetes.

Methods: We propose stacks of convolutional neural network and long short-term memory units to predict BG level for 30-, 60-, and 90-minute prediction horizon (PH), given historical glucose measurements, meal information, and insulin intakes. The evaluation was performed on two data sets, Replace-BG and DIAdvisor, representative of free-living conditions and in-hospital setting, respectively.

Results: For 90-minute PH, our model obtained mean absolute error of 17.30 ± 2.07 and 18.23 ± 2.97 mg/dL, root mean square error of 23.45 ± 3.18 and 25.12 ± 4.65 mg/dL, coefficient of determination of 84.13 ± 4.22% and 82.34 ± 4.54%, and in terms of the continuous glucose-error grid analysis 94.71 ± 3.89% and 91.71 ± 4.32% accurate predictions, 1.81 ± 1.06% and 2.51 ± 0.86% benign errors, and 3.47 ± 1.12% and 5.78 ± 1.72% erroneous predictions, for Replace-BG and DIAdvisor data sets, respectively.

Conclusion: Our investigation demonstrated that our method achieved superior glucose forecasting compared with existing approaches in the literature, and thanks to its generalizability showed potential for real-life applications.

Keywords: artificial deep neural networks; continuous glucose monitoring (CGM); convolutional neural network (CNN); decision support systems; glucose forecasting; long short-term memory (LSTM).

Figure 1.

The proposed CNN-LSTM architecture for 90-minute PH BG prediction. The input sample, with length three times the length of the PH, consisting of preprocessed segments of data such as CGM, CHO, and insulin intakes, is passed to the multilayer CNN block. After flattening the resulting feature map, it is input into a two-layer LSTM block for temporal dynamic analysis. Finally, high-level features are evaluated using two fully connected layers, resulting in the predicted glucose values. Abbreviations: CNN, convolutional neural network; LSTM, long short-term memory; PH, prediction horizon; BG, blood glucose; CGM, continuous glucose monitoring; CHO, carbohydrate.

Figure 2.

A representative example of CG-EGA with P-EGA (left) and R-EGA (right) components. Abbreviations: CG-EGA, continuous glucose-error grid analysis; P-EGA, point-error grid analysis; R-EGA, rate-error grid analysis.

Figure 3.

Actual CGM value compared with the predictions for 60-minute PH by the ARX, SVR, LSTM, CRNN, and CNN-LSTM models for a representative patient from the Replace-BG data set, for a three-day duration. Abbreviations: CGM, continuous glucose monitoring; PH, prediction horizon; ARX, autoregressive with exogenous inputs; SVR, support vector regression; LSTM, long short-term memory; CRNN, convolutional recurrent neural network; CNN, convolutional neural network; BG, blood glucose.

Figure 4.

Performance evaluation of the proposed model on the Replace-BG (top panel) and DIAdvisor (bottom panel) data sets for three different PHs over test patients. Left: MAE, middle: RMSE, and right: R2. In each boxplot, the central mark is the median, the bottom and top edges are 25th and 75th percentiles, respectively. Whiskers extend to the most extreme data points not considered outliers. Abbreviations: BG, blood glucose; PH, prediction horizon; MAE, mean absolute error; RMSE, root mean square error; R2, coefficient of determination.

Figure 5.

Representative examples of the CGM prediction by the proposed CNN-LSTM for different PHs, for six patients from Replace-BG data set. Abbreviations: CGM, continuous glucose monitoring; CNN, convolutional neural network; LSTM, long short-term memory; PH, prediction horizon; BG, blood glucose.

Figure 6.

Patient-wise performance evaluation of the proposed model on the Replace-BG (top panel) and DIAdvisor (bottom panel) data sets for three different PHs over the test data set. Left: MAE, middle: RMSE, and right: R2. In each boxplot, the central mark is the median. Abbreviations: BG, blood glucose; PH, prediction horizon; MAE, mean absolute error; RMSE, root mean square error; R2, coefficient of determination.