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Deep learning framework for detection of hypoglycemic episodes in children with type 1 diabetes

机译：患有1型糖尿病儿童低血糖发作的深度学习框架

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Most Type 1 diabetes mellitus (T1DM) patients have hypoglycemia problem. Low blood glucose, also known as hypoglycemia, can be a dangerous and can result in unconsciousness, seizures and even death. In recent studies, heart rate (HR) and correct QT interval (QTc) of the electrocardiogram (ECG) signal are found as the most common physiological parameters to be effected from hypoglycemic reaction. In this paper, a state-of-the-art intelligent technology namely deep belief network (DBN) is developed as an intelligent diagnostics system to recognize the onset of hypoglycemia. The proposed DBN provides a superior classification performance with feature transformation on either processed or un-processed data. To illustrate the effectiveness of the proposed hypoglycemia detection system, 15 children with Type 1 diabetes were volunteered overnight. Comparing with several existing methodologies, the experimental results showed that the proposed DBN outperformed and achieved better classification performance.
机译：大多数1型糖尿病（T1DM）患者有低血糖问题。低血糖，也称为低血糖，可能是一种危险的，可以导致无意识，癫痫发作甚至死亡。在最近的研究中，心电图（ECG）信号的心率（HR）和QT间隔（QTC）被发现是从降血糖反应进行的最常见的生理参数。在本文中，最先进的智能技术即深度信仰网络（DBN）被开发为智能诊断系统，以识别低血糖的开始。该提议的DBN提供了卓越的分类性能，具有在任何处理或未处理数据上的功能转换。为了说明所提出的低血糖检测系统的有效性，15型糖尿病的15名儿童志愿一夜。与若干现有方法相比，实验结果表明，所提出的DBN优于并实现了更好的分类性能。

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