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

Autor
Monte, E.
Tipus d'activitat
Article en revista
Revista
Artificial intelligence in medicine
Data de publicació
2011-10
Volum
53
Número
2
Pàgina inicial
127
Pàgina final
138
DOI
https://doi.org/10.1016/j.artmed.2011.05.001 Obrir en finestra nova
Repositori
http://hdl.handle.net/2117/14361 Obrir en finestra nova
Resum
Objective: This work presents a system for a simultaneous non-invasive estimate of the blood glucose level (BGL) and the systolic (SBP) and diastolic (DBP) blood pressure, using a photoplethysmograph (PPG) and machine learning techniques. The method is independent of the person whose values are being measured and does not need calibration over time or subjects. Methodology: The architecture of the system consists of a photoplethysmograph sensor, an activity detection module, a signal processing ...
Citació
Monte, E. Non-invasive estimate of blood glucose and blood pressure from a photoplethysmograph by means of machine learning techniques. "Artificial intelligence in medicine", Octubre 2011, vol. 53, núm. 2, p. 127-138.
Grup de recerca
IDEAI-UPC Intelligent Data Science and Artificial Intelligence
TALP - Centre de Tecnologies i Aplicacions del Llenguatge i la Parla
VEU - Grup de Tractament de la Parla

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