基于ECG信号的高精度血糖监测

连续血糖监测在糖尿病管理中具有重要的意义。目前糖尿病患者主要通过指尖采血或植入式微创传感器监测血糖，但上述方法存在疼痛、成本昂贵、易感染等问题，因此，无创监测是实现连续血糖监测的理想技术。本文利用心电(ECG)信号，提出了一种血糖水平无创监测的方法:通过获取12名志愿者共60 d 756160个ECG周期信号，利用递归滤波器实现ECG信号的滤波，并采用卷积神经网络和长短期记忆网络相结合(CNN-LSTM)的方法，实现了血糖水平的十分类监测，并通过实验探索了个体建模和群体建模2种建模方式的差异。结果表明，在个体建模和群体建模的条件下，血糖监测精确率分别约达到80%和88%。其中群体建模10分类的F1值可达到0.95、0.88、0.91、0.85、0.92、0.88、0.86、0.86、0.87和0.86。研究表明，本文提出的基于ECG的无创血糖监测方法为实现血糖水平的实时、精准监测提供了一种有力的理论支撑与技术指导。 

High accuracy blood glucose monitoring based on ECG signals

Continuous glucose monitoring is important in the management of diabetes. According to statistics, diabetes is the third chronic non-infectious disease that seriously endangers people's health, followed by tumor as well as cardiovascular and cerebrovascular diseases. In 2019, globally, there were a total of 460 million diabetics aged 20–79 years, which accounted for 9.1% of the total population in this cohort. Each figure is projected to increase to 592 million and by 10.1% respectively by 2035. Currently, the methods of blood glucose monitoring can be divided into invasive, minimally invasive, and noninvasive. The main methods for blood glucose monitoring include irregular sampling of fingertip blood or consecutive measurement of interstitial fluid glucose based on implantable sensors. However, these methods have some limitations, which include pain sensation, high cost, short service life, and susceptibility. Patients need to measure their blood glucose frequently. Invasive and minimally invasive monitoring will cause physical and psychological pain. Therefore, noninvasive monitoring is one of the most promising techniques for continuous monitoring of blood glucose, and it has a broad market prospect. In this study, the electrocardiogram (ECG signals) were used to achieve the noninvasive monitoring of blood glucose levels. First, 756160 ECG periodic signals of 12 volunteers for 60 d were obtained from the experiment. Second, the ECG signals were preprocessed using an infinite impulse response filter. Furthermore, a method combining convolutional neural networks and long short-term memory networks (CNN-LSTM) was proposed for blood glucose monitoring. In Addition, two modeling methods (individual modeling and group modeling) were investigated in this study. The results show that the precision of blood glucose monitoring under the condition of individual and group modeling is 80% and 88%, respectively. The F1-score of the group modeling can reach 0.95, 0.88, 0.91, 0.85, 0.92, 0.88, 0.86, 0.86, 0.87, and 0.86. Therefore, this study indicates that the proposed method based on ECG signals can provide powerful theoretical support and technical guidance for real-time and accurate blood glucose monitoring.