Benchmarking Hypoglycemia Classification Using Quality-Enhanced DiaData

Medical data analysis provides valuable insights into patient contexts, supporting personalized treatments and preventive strategies. Reliable analysis requires large volumes of high-quality data, as outliers can distort results and missing values lead to information loss. Notably, for Type 1 Diabetes (T1D), data analysis explores relationships between demographics, sensor data, and behavior. To address limited data volume, DiaData-an integration of 15 separate datasets containing glucose values from 2510 subjects with T1D-was previously introduced. This study improves the quality of DiaData by identifying outliers and imputing missing values. In particular, we make the following contributions: 1) Sensor errors are determined with the interquartile range (IQR) approach and replaced with missing values. Outlier removal leads to less bias toward misleading values. 2) Gaps are classified by length to impute small gaps ($\le$ 25min) with linear interpolation and larger gaps ($\ge$30 and $\le$ 120min) with Stineman interpolation. A visual comparison shows that Stineman interpolation provides more realistic glucose estimates than linear interpolation for larger gaps. 3) After data cleaning, the correlation between glucose and heart rate is analyzed, reporting a moderate relation between 15 and 60 minutes before hypoglycemia ($\le$ 70mg/dL). 4) Finally, a benchmark for hypoglycemia classification is provided with a state-of-the-art Fully Convolutional Network (FCN). The model is trained with the main database and subdatabase II of DiaData to classify hypoglycemia onset up to 2 hours in advance. Training with more data improves performance by 3% while using quality-refined data yields a 4% gain compared to raw data.