Type 1 diabetes (T1D) is an autoimmune disorder that leads to the destruction of insulin-producing cells, resulting in insulin deficiency, as to why the affected individuals depend on external insulin injections. However, insulin can decrease blood glucose levels and can cause hypoglycemia. Hypoglycemia is a severe event of low blood glucose levels ($\le$70 mg/dL) with dangerous side effects of dizziness, coma, or death. Data analysis can significantly enhance diabetes care by identifying personal patterns and trends leading to adverse events. Especially, machine learning (ML) models can predict glucose levels and provide early alarms. However, diabetes and hypoglycemia research is limited by the unavailability of large datasets. Thus, this work systematically integrates 15 datasets to provide a large database of 2510 subjects with glucose measurements recorded every 5 minutes. In total, 149 million measurements are included, of which 4% represent values in the hypoglycemic range. Moreover, two sub-databases are extracted. Sub-database I includes demographics, and sub-database II includes heart rate data. The integrated dataset provides an equal distribution of sex and different age levels. As a further contribution, data quality is assessed, revealing that data imbalance and missing values present a significant challenge. Moreover, a correlation study on glucose levels and heart rate data is conducted, showing a relation between 15 and 55 minutes before hypoglycemia.

翻译：1型糖尿病（T1D）是一种导致胰岛素分泌细胞破坏的自身免疫性疾病，引发胰岛素缺乏，因此患者需依赖外源性胰岛素注射。然而，胰岛素在降低血糖水平的同时可能引发低血糖。低血糖是指血糖水平严重降低（≤70 mg/dL）的危急事件，可能导致头晕、昏迷甚至死亡等危险后果。数据分析能够通过识别导致不良事件的个体化模式和趋势，显著改善糖尿病护理。特别是，机器学习模型可预测血糖水平并提供早期预警。然而，糖尿病和低血糖研究因缺乏大规模数据集而受限。为此，本文系统整合了15个数据集，构建了一个包含2510名受试者、每5分钟记录一次血糖测量值的大型数据库。该数据库共涵盖1.49亿次测量值，其中4%属于低血糖区间。此外，我们提取了两个子数据库：子数据库I包含人口统计学信息，子数据库II包含心率数据。该整合数据集在性别和不同年龄层上具有均衡分布。作为进一步贡献，我们对数据质量进行了评估，发现数据不平衡和缺失值构成重大挑战。同时，我们开展了血糖水平与心率数据的相关性分析，揭示了低血糖发生前15至55分钟期间两者之间的关联。