Alhaddad Ahmad Yaser, Aly Hussein, Gad Hoda, Al-Ali Abdulaziz, Sadasivuni Kishor Kumar, Cabibihan John-John, Malik Rayaz A
Department of Mechanical and Industrial Engineering, Qatar University, Doha, Qatar.
KINDI Center for Computing Research, Qatar University, Doha, Qatar.
Front Bioeng Biotechnol. 2022 May 12;10:876672. doi: 10.3389/fbioe.2022.876672. eCollection 2022.
Diabetes mellitus is characterized by elevated blood glucose levels, however patients with diabetes may also develop hypoglycemia due to treatment. There is an increasing demand for non-invasive blood glucose monitoring and trends detection amongst people with diabetes and healthy individuals, especially athletes. Wearable devices and non-invasive sensors for blood glucose monitoring have witnessed considerable advances. This review is an update on recent contributions utilizing novel sensing technologies over the past five years which include electrocardiogram, electromagnetic, bioimpedance, photoplethysmography, and acceleration measures as well as bodily fluid glucose sensors to monitor glucose and trend detection. We also review methods that use machine learning algorithms to predict blood glucose trends, especially for high risk events such as hypoglycemia. Convolutional and recurrent neural networks, support vector machines, and decision trees are examples of such machine learning algorithms. Finally, we address the key limitations and challenges of these studies and provide recommendations for future work.
糖尿病的特征是血糖水平升高，然而糖尿病患者也可能因治疗而发生低血糖。糖尿病患者和健康个体，尤其是运动员，对无创血糖监测和趋势检测的需求日益增加。用于血糖监测的可穿戴设备和无创传感器已经取得了相当大的进展。本综述是对过去五年利用新型传感技术所做贡献的更新，这些技术包括心电图、电磁、生物阻抗、光电容积脉搏波描记术和加速度测量以及体液葡萄糖传感器，用于监测血糖和趋势检测。我们还综述了使用机器学习算法预测血糖趋势的方法，特别是针对低血糖等高风险事件。卷积神经网络和循环神经网络、支持向量机和决策树就是这类机器学习算法的例子。最后，我们阐述了这些研究的关键局限性和挑战，并为未来的工作提供建议。
