Incorporating the Effect of Behavioral States in Multi-Step Ahead Deep Learning Based Multivariate Predictors for Blood Glucose Forecasting in Type 1 Diabetes

Abstract

Behavioral factors can affect the blood glucose (BG) levels in people with type 1 diabetes (T1D), therefore, their effects need to be incorporated in blood glucose management for these individuals. Accordingly, in this work, we study the effect of two behavioral states, physical activity (PA) and stress state (SS), on BG fluctuations in individuals with T1D. We provide two methods for quantifying biomarkers related to PA and SS using raw acceleration (ACC) and electrodermal activity (EDA) data collected with a wearable device. We evaluate the impact of PA and SS on BG fluctuation by adding the derived behavior-related biomarkers in two cutting-edge deep learning-based glucose predictive models, a long short-term memory (LSTM) and a convolutional neural network (CNN)-LSTM network, for prediction horizons (PHs) of 30, 60 and 90 min. Through an ablation study, we demonstrate that incorporating the estimated behavior-related biomarkers improves the BG predictive model’s performance obtaining mean absolute error (MAE) 9.13 ± 0.95, 17.75 ± 1.93 and 31.85 ± 2.88 in [mg/dL], root mean square error (RMSE), 12.35 ± 1.06, 24.71 ± 2.31 and 41.64 ± 4.12 in [mg/dL], and coefficient of determination (R2), 95.34 ± 3.34, 78.87 ± 4.35 and 60.11 ± 4.76 in [%], for the LSTM model; and MAE 9.37 ± 0.88, 17.87 ± 1.67 and 29.47 ± 2.13 in [mg/dL], RMSE 12.51 ± 1.40, 24.37 ± 2.49 and 39.52 ± 3.89 in [mg/dL], and R2 94.65 ± 3.90, 78.37 ± 4.11 and 61.12 ± 4.30 in [%], for the CNN-LSTM model, respectively, across all PHs. Additionally, we illustrate the generalizability of the proposed models by performing both population- and patient-wise.

Keywords

• blood glucose management
• continuous glucose monitoring (CGM)
• glucose forecasting
• convolutional neural network (CNN)
• long short-term memory (LSTM)
• physical activity index (PAI)