Non-invasive estimate of blood glucose and blood pressure from a photoplethysmograph by means of machine learning techniques

By Enric Monte‐Moreno. Artif. Intell. Medicine, 2011

301 citations recorded by the source corpus.

Subfields: Biomedical Engineering, Biophysics, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/aeace2b62121e2a03e46cd9cddd0d2f528d858de

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In Biomedical Engineering

1. Amin Abbosh

Citations in the corpus

• The Progress of Glucose Monitoring—A Review of Invasive to Minimally and Non-Invasive Techniques, Devices and Sensors (2019, background): “Finger-pricking SMBG devices are currently the most reliable and accurate devices for self-monitoring due to the relative simplicity of the measuring procedure, and their reliance on capillary blood to obtain accurate glucose readings [31].”