Key result
Noninvasive glucose measurement approaches using machine learning, neural networks, and correlation with heart rate variability were analyzed as a new research and development trend.
Why the study?
Usual blood glucose monitoring requires invasive finger pricking with a lancet, which can be painful and burdensome as a daily routine.
Design
Review
Authors
Loading...
Caution against clinical use; leaves open the viability of ML-HRV approaches pending validation trials.
This review highlights emerging trends in noninvasive glucose monitoring using machine learning and electrocardiogram/heart rate variability correlations.
Gušev et al. (2020) conducted a review in Diabetes. Noninvasive glucose measurement was evaluated. Noninvasive glucose measurement approaches using machine learning, neural networks, and correlation with heart rate variability were analyzed as a new research and development trend.
Synapse has enriched 4 closely related papers on similar clinical questions. Consider them for comparative context: