Zeynali Mahdi, Alipour Khalil, Tarvirdizadeh Bahram, Ghamari Mohammad
Advanced Service Robots (ASR) Laboratory, Department of Mechatronics Engineering, School of Intelligent Systems Engineering, College of Interdisciplinary Science and Technology, University of Tehran, Tehran, Iran.
Department of Electrical Engineering, California Polytechnic State University, San Luis Obispo, California, USA.
Sci Rep. 2025 Jan 2;15(1):581. doi: 10.1038/s41598-024-84265-8.
Accurate and continuous blood glucose monitoring is essential for effective diabetes management, yet traditional finger pricking methods are often inconvenient and painful. To address this issue, photoplethysmography (PPG) presents a promising non-invasive alternative for estimating blood glucose levels. In this study, we propose an innovative 1-second signal segmentation method and evaluate the performance of three advanced deep learning models using a novel dataset to estimate blood glucose levels from PPG signals. We also extend our testing to additional datasets to assess the robustness of our models against unseen distributions, thereby providing a comprehensive evaluation of the models' generalizability and specificity and accuracy. Initially, we analyzed 10-second PPG segments; however, our newly developed 1-second signal segmentation technique proved to significantly enhance accuracy and computational efficiency. The selected model, after being optimized and deployed on an embedded device, achieved immediate blood glucose estimation with a processing time of just 6.4 seconds, demonstrating the method's practical applicability. The method demonstrated strong generalizability across different populations. Training data was collected during surgery and anesthesia, and the method also performed successfully in normal states using a separate test dataset. The results showed an average root mean squared error (RMSE) of 19.7 mg/dL, with 76.6% accuracy within the A zone and 23.4% accuracy within the B zone of the Clarke Error Grid Analysis (CEGA), indicating a 100% clinical acceptance. These findings demonstrate that blood glucose estimation using 1-second PPG signal segments not only outperforms the traditional 10-second segments, but also provides a more convenient and accurate alternative to conventional monitoring methods. The study's results highlight the potential of this approach for non-invasive, accurate, and convenient diabetes management, ultimately offering improved health management.
准确且持续的血糖监测对于有效的糖尿病管理至关重要，但传统的指尖采血方法往往不方便且令人痛苦。为了解决这个问题，光电容积脉搏波描记法（PPG）为估算血糖水平提供了一种有前景的非侵入性替代方法。在本研究中，我们提出了一种创新的1秒信号分割方法，并使用一个新颖的数据集评估三种先进深度学习模型从PPG信号估算血糖水平的性能。我们还将测试扩展到其他数据集，以评估我们的模型对未见分布的稳健性，从而全面评估模型的通用性、特异性和准确性。最初，我们分析10秒的PPG片段；然而，我们新开发的1秒信号分割技术被证明能显著提高准确性和计算效率。所选模型在经过优化并部署到嵌入式设备上后，实现了即时血糖估算，处理时间仅为6.4秒，证明了该方法的实际适用性。该方法在不同人群中表现出很强的通用性。训练数据是在手术和麻醉期间收集的，并且该方法在正常状态下使用单独的测试数据集也成功运行。结果显示平均均方根误差（RMSE）为19.7mg/dL，在克拉克误差网格分析（CEGA）的A区准确率为76.6%，在B区准确率为23.4%，表明临床接受率为100%。这些发现表明，使用1秒PPG信号片段进行血糖估算不仅优于传统的10秒片段，而且为传统监测方法提供了一种更方便、准确的替代方法。该研究结果突出了这种方法在非侵入性、准确且方便的糖尿病管理方面的潜力，最终改善健康管理。
