Fellah Arbi Khadidja, Soulimane Sofiane, Saffih Faycal, Bechar Mohammed Amine, Azzoug Omar
Biomedical Engineering Laboratory, University of Tlemcen, Tlemcen, Algeria.
Centre for the Development of Advanced Technologies (CDTA) at Setif, University of Setif1, EL-Baz Campus, 19000, Setif, Algeria.
Phys Eng Sci Med. 2023 Mar;46(1):255-264. doi: 10.1007/s13246-022-01214-3. Epub 2023 Jan 3.
Successful self-management of diabetes requires Continuous Glucose Monitors (CGMs). These CGMs have several limitations such as being invasive, expensive and limited in terms of use. Many techniques, in vain, have been proposed to overcome these limitations. Nowadays, with the help of the Internet of Medical Things (IoMT) technologies, researchers are working to find alternative solutions. They succeed to predict hypoglycemia and hyperglycemia peaks using Electrocardiogram (ECG) signals. However, they failed to use it to estimate the Blood Glucose Concentration (BGC) directly and in real time. Three patients with 08 days of measurements from the D1namo dataset contributed to the study. A new technique has been proposed to estimate the BGC curves based on ECG signals. We used a convolutional neural network to segment the different regions of ECG signals as well as we extracted ECG features that were required for the next step. Then, five regression models have been employed to estimate BGC using as input sixth ECG parameters. We were able to segment the ECG signals with an accuracy of 94% using the convolutional neural network algorithm. The best performance among all simulated models was provided by Exponential Gaussian Process Regression (GPR) with Root Mean Squared Error (RMSE) values of 0.32, 0.41, 0.67 and R-squared (R) values of 98%, 80%, and 70% for patients 01, 02 and 03 respectively. The method indicates the potential use of ECG wearable devices as non-invasive for continuous blood glucose monitoring, which is affordable and durable.
糖尿病的成功自我管理需要持续葡萄糖监测仪（CGM）。这些CGM有几个局限性，比如具有侵入性、价格昂贵且使用受限。人们提出了许多技术来克服这些局限性，但都徒劳无功。如今，在医疗物联网（IoMT）技术的帮助下，研究人员正在努力寻找替代解决方案。他们成功地利用心电图（ECG）信号预测低血糖和高血糖峰值。然而，他们未能直接实时地利用ECG信号来估计血糖浓度（BGC）。来自D1namo数据集的3名患者提供了8天的测量数据，为该研究做出了贡献。一种基于ECG信号估计BGC曲线的新技术被提了出来。我们使用卷积神经网络对ECG信号的不同区域进行分割，并提取下一步所需的ECG特征。然后，采用了5种回归模型，将6个ECG参数作为输入来估计BGC。使用卷积神经网络算法，我们能够以94%的准确率分割ECG信号。在所有模拟模型中，指数高斯过程回归（GPR）表现最佳，患者1、患者2和患者3的均方根误差（RMSE）值分别为0.32、0.41、0.67，决定系数（R²）值分别为98%、80%和70%。该方法表明，ECG可穿戴设备有潜力作为非侵入性设备用于连续血糖监测，而且价格实惠且耐用。
