  Blood glucose (BG) management is crucial for type-1 diabetes patients
resulting in the necessity of reliable artificial pancreas or insulin infusion
systems. In recent years, deep learning techniques have been utilized for a
more accurate BG level prediction system. However, continuous glucose
monitoring (CGM) readings are susceptible to sensor errors. As a result,
inaccurate CGM readings would affect BG prediction and make it unreliable, even
if the most optimal machine learning model is used. In this work, we propose a
novel approach to predicting blood glucose level with a stacked Long short-term
memory (LSTM) based deep recurrent neural network (RNN) model considering
sensor fault. We use the Kalman smoothing technique for the correction of the
inaccurate CGM readings due to sensor error. For the OhioT1DM dataset,
containing eight weeks' data from six different patients, we achieve an average
RMSE of 6.45 and 17.24 mg/dl for 30 minutes and 60 minutes of prediction
horizon (PH), respectively. To the best of our knowledge, this is the leading
average prediction accuracy for the ohioT1DM dataset. Different physiological
information, e.g., Kalman smoothed CGM data, carbohydrates from the meal, bolus
insulin, and cumulative step counts in a fixed time interval, are crafted to
represent meaningful features used as input to the model. The goal of our
approach is to lower the difference between the predicted CGM values and the
fingerstick blood glucose readings - the ground truth. Our results indicate
that the proposed approach is feasible for more reliable BG forecasting that
might improve the performance of the artificial pancreas and insulin infusion
system for T1D diabetes management.
