用智能手表的光电容积脉搏波信号预测血糖，实现无创监测。
An Exploratory Study of Blood Glucose Estimation from Photoplethysmography Signals using Machine Learning
- 基于智能手表的PPG信号与CGM数据配对，构建非侵入式血糖估计模型。
- 初步实验显示信号中存在可预测血糖的潜在特征，需更大样本验证。
- 适合可穿戴健康监测、糖尿病管理及机器学习应用研究者参考。
糖尿病及极端血糖水平是当今全球面临的主要健康问题。尽管连续血糖监测（CGM）已成为糖尿病管理与血糖监控的有效技术，但传统CGM依赖皮肤穿刺，具有刺激、硬结等风险。这凸显了开发准确且非侵入式CGM方法以实现规模化部署的迫切需求。随着各类传感技术在智能手表等可穿戴设备中的集成，现在可非侵入式地持续监测光电容积脉搏波（PPG）信号。结合CGM对血糖的连续监测与智能手表对PPG的持续采集，为构建基于机器学习和深度学习的血糖估计模型提供了可能。本文首次发布一个配对数据集，包含来自智能手表的连续PPG信号及通过CGM记录的血糖值。同时报告了在此数据集上的初步实验探索结果。初步结果显示，可能存在可预测血糖的信号特征，但仍需更多个体、更大规模的数据进一步验证。该数据集可通过https://zenodo.org/records/20577959获取。
原文摘要 · Abstract (English)
Diabetes and extreme blood sugar levels are some of the major health problems faced by humans today across the world. While Continuous Glucose Monitoring (CGM) has emerged as an effective technology for management of diabetes as well as for monitoring blood sugar levels, this technology has traditionally been invasive (that is, requiring the piercing of the skin) and carries the risk of irritation, induration, etc. This highlights the need for accurate and non-invasive CGM methods that can be deployed at scale. With the emergence of various sensing technologies and their integration in wearables like the smart-watch, we now have the capability to continuously monitor body signals like the Photoplethysmogram (PPG) in a non-invasive manner. Having the ability to continuously monitor blood glucose through CGMs and continuously monitor PPG signals through a smart-watch offers an opportunity to get dense data on these two, opening the possibility of building machine learning and deep learning based models to estimate blood glucose level from PPG signals. In this work, we first present a paired dataset comprising continuous PPG signals from a smartwatch along with glucose values recorded using a CGM device. We also present the results of some preliminary experimental explorations performed on our dataset. These preliminary results suggest that some predictive signals may exist, though more exploration is needed with more data from a larger number of individuals. The dataset can be accessed at https://zenodo.org/records/20577959
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