Hypoglycemia in Type 1 Diabetes (T1D) refers to a
condition where blood glucose (BG) levels drop to abnormally
low levels, typically below 70 mg/dL. This can occur when there is
an excessive amount of insulin relative to the blood glucose level,
leading to an imbalance that can be dangerous and potentially
life-threatening if not promptly treated. The availability of large
amounts of data from continuous glucose monitoring (CGM),
insulin doses, carbohydrate intake, and additional vital signs,
together with deep learning (DL) techniques, has revolutionized
algorithmic approaches for BG prediction in T1D, achieving
superior performance. In our study, we employed a Long ShortTerm Memory (LSTM) neural network architecture to predict
hypoglycemia events in patients with T1D. For the training and
testing, we utilized the OhioT1DM (2018) dataset. In addition,
real-time data collected from an individual patient for the evaluation. This patient utilized the CGM FreeStyle Libre (FSL) system,
along with a smartwatch to monitor step count. The LSTM
model exhibited performance demonstrating exceptional levels
of sensitivity, specificity, and accuracy scores of 97.09%, 94.17%,
and 95.63%, respectively, when assessed using the Ohio test
dataset. Our research provides strong evidence supporting the
system’s efficacy in managing hypoglycemia events in individuals
diagnosed with T1D.