Kim Jongdeog, Kim Bong Kyu, Park Mi-Ryong, Cho Hyoyoung, Huh Chul
Digital Biomedical Research Division, Electronics and Telecommunications Research Institute (ETRI), Daejeon 34129, Republic of Korea.
Terrestrial and Non-Terrestrial Integrated Telecommunications Research Laboratory, Electronics and Telecommunications Research Institute (ETRI), Daejeon 34129, Republic of Korea.
Biosensors (Basel). 2025 Jun 24;15(7):406. doi: 10.3390/bios15070406.
This study investigates a noninvasive continuous glucose monitoring (NI-CGM) system optimized for earlobe application, leveraging the site's anatomical advantages-absence of bone, muscle, and thick skin-for enhanced optical transmission. The system integrates multimodal sensing, combining near-infrared (NIR) diffuse transmission with temperature and pressure sensors. A novel Multi-Wavelength Slope Efficiency Near-Infrared Spectroscopy (MW-SE-NIRS) method is introduced, enhancing noise robustness through the slope efficiency-based parameterization of NIR signal dynamics. By employing three NIR wavelengths with distinct scattering and absorption properties, the method improves glucose detection reliability, addressing tissue heterogeneity and physiological noise in noninvasive monitoring. To validate the feasibility, a pilot clinical trial enrolled five participants with normal or pre-diabetic glucose profiles. Continuous glucose data capturing pre- and postprandial variations were analyzed using a 1D convolutional neural network (Conv1D). For three subjects under stable physiological conditions, the model achieved 97.0% Clarke error grid (CEG) A-Zone accuracy and a mean absolute relative difference (MARD) of 5.2%. Across all participants, results showed 90.9% CEG A-Zone accuracy and a MARD of 8.4%, with performance variations linked to individual factors such as earlobe thickness variability and physical activity. These outcomes demonstrate the potential of the MW-SE-NIRS system for noninvasive glucose monitoring and highlight the importance of future work on personalized modeling, sensor optimization, and larger-scale clinical validation.
本研究调查了一种针对耳垂应用进行优化的无创连续血糖监测（NI-CGM）系统，该系统利用耳垂部位无骨骼、肌肉且皮肤薄的解剖学优势来增强光传输。该系统集成了多模态传感，将近红外（NIR）漫透射与温度和压力传感器相结合。引入了一种新颖的多波长斜率效率近红外光谱（MW-SE-NIRS）方法，通过基于斜率效率的近红外信号动态参数化提高噪声鲁棒性。通过采用具有不同散射和吸收特性的三个近红外波长，该方法提高了葡萄糖检测的可靠性，解决了无创监测中的组织异质性和生理噪声问题。为验证其可行性，一项初步临床试验招募了五名血糖水平正常或处于糖尿病前期的参与者。使用一维卷积神经网络（Conv1D）分析了捕获餐后前后变化的连续血糖数据。对于三名处于稳定生理条件下的受试者，该模型实现了97.0%的克拉克误差网格（CEG）A区准确率和5.2%的平均绝对相对差异（MARD）。在所有参与者中，结果显示CEG A区准确率为90.9%，MARD为8.4%，其性能差异与耳垂厚度变化和身体活动等个体因素有关。这些结果证明了MW-SE-NIRS系统在无创血糖监测方面的潜力，并突出了未来在个性化建模、传感器优化和大规模临床验证方面工作的重要性。
