Key result
Blood glucose estimation using wearable devices utilizing PPG signals achieved 90% accuracy in predicting glucose levels in type 2 diabetic patients.
Why the study?
Pain from finger-pricking blood glucose monitoring reduces patient compliance, prompting the need for reliable, mobile, and noninvasive alternatives using wearable optical signals like photoplethysmograms.
Does a wearable device utilizing PPG signals and machine learning models accurately estimate blood glucose in type 2 diabetic patients?
Observational (n=9)
Does a wearable device utilizing PPG signals and machine learning models accurately estimate blood glucose in type 2 diabetic patients?
Noninvasive blood glucose estimation using wearable PPG devices and machine learning models is feasible and achieves 90% accuracy in a small cohort of type 2 diabetic patients.
May enable non-invasive glucose monitoring in T2D; hypothesis-generating and requires larger validation trials before clinical adoption.
Diabetics should monitor their blood glucose daily to prevent severe acute complications. However, the pain sensation due to finger pricking is not only an inconvenience to patients, but can also reduce compliance with diabetes management. A reliable, mobile, easy-to-use, and noninvasive glucometer can address this problem. Noninvasive optical signals, such as those used to obtain a photoplethysmogram (PPG), have recently been used to measure human physiological and vascular conditions and have been adopted to estimate glucose. In this study, we conducted a clinical trial to acquire the PPG signals using wearable devices from nine type 2 diabetic patients. The global and personalized models were built using random forest and adaboost regression models. The accuracy of the models was determined by the tenfold cross validation and leave-one-out validation approaches. The results show that it is feasible to attain 90% accurate glucose predictions. Therefore, diabetic patients can use a wearable noninvasive glucometer utilizing the PPG signals.
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Tsai et al. (2019) conducted an observational in Type 2 diabetes (n=9). Wearable devices utilizing PPG signals for blood glucose estimation was evaluated on Accuracy of glucose predictions. Blood glucose estimation using wearable devices utilizing PPG signals achieved 90% accuracy in predicting glucose levels in type 2 diabetic patients.
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