Continuous monitoring of the blood glucose levels is essential and critical for controlling diabetes and its complications. With the improvement of the measurement accuracy of the acquisition devices developed in recent decades, developing the optical-based methods for performing the non-invasive blood glucose estimation for the consumer applications becomes very important. The authors’ previous work is based on the heart rate variability of the electrocardiogram and the existing method is based on applying the random forest to the features extracted from the photoplethysmogram. However, the accuracies of these two methods are not very high. In this study, a joint empirical mode decomposition and exponential function estimation approach is proposed for estimating the mean value of a photoplethysmogram acquired from a wearable non-invasive blood glucose device. Also, the exponential function fitting approach is employed for estimating the blood glucose levels via an L ₁ norm formulation. The computer numerical simulation results show that the estimation accuracy based on their proposed method is higher than that based on the state-of-the-art methods. Therefore, their proposed method can be employed for performing blood glucose estimation effectively.

中文翻译：

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联合经验模式分解，指数函数估计和 大号₁，用于估计光电容积描记和血糖水平的平均值标准方法

连续监测血糖水平对于控制糖尿病及其并发症至关重要。随着近几十年来开发的采集设备的测量精度的提高，开发用于消费者应用的用于执行无创血糖估计的基于光学的方法变得非常重要。作者的先前工作是基于心电图的心率变异性，而现有方法是基于将随机森林应用于从光电容积描记图提取的特征。但是，这两种方法的准确性不是很高。在这个研究中，提出了一种基于经验模式分解和指数函数的联合估计方法，用于估计从可穿戴式无创血糖仪获取的光体积描记图的平均值。同样，采用指数函数拟合方法来估计血糖水平。大号 ₁规范制定。计算机数值模拟结果表明，基于他们提出的方法的估计精度要高于基于最新方法的估计精度。因此，他们提出的方法可用于有效地进行血糖估计。