FPGA Based Smart System for Non Invasive Blood Glucose Sensing Using Photoplethysmography and Online Correction of Motion Artifact
- 28 October 2016
- book chapter
- Published by Springer Nature
- p. 1-21
Abstract
No abstract availableKeywords
This publication has 13 references indexed in Scilit:
- Non-invasive estimate of blood glucose and blood pressure from a photoplethysmograph by means of machine learning techniquesArtificial Intelligence in Medicine, 2011
- Blood Glucose Prediction Using Artificial Neural Networks Trained with the AIDA Diabetes Simulator: A Proof-of-Concept Pilot StudyJournal of Electrical and Computer Engineering, 2011
- Development of a FPGA based fuzzy neural network system for early diagnosis of critical health condition of a patientComputers in Biology and Medicine, 2010
- Artificial Neural Network Algorithm for Online Glucose Prediction from Continuous Glucose MonitoringDiabetes Technology & Therapeutics, 2010
- Numerical and Clinical Accuracy of a Continuous Glucose Monitoring System during Intravenous Insulin Therapy in the Surgical and Burn Intensive Care UnitsJournal of Diabetes Science and Technology, 2009
- Continuous Glucose Monitoring: An Overview of Its DevelopmentDiabetes Technology & Therapeutics, 2009
- Comparison between transmittance and reflectance measurements in glucose determination using near infrared spectroscopyJournal of Biomedical Optics, 2006
- Intensive Diabetes Treatment and Cardiovascular Disease in Patients with Type 1 DiabetesNew England Journal of Medicine, 2005
- Scattering and Absorption Effects in the Determination of Glucose in Whole Blood by Near-Infrared SpectroscopyAnalytical Chemistry, 2005
- Evaluating Clinical Accuracy of Systems for Self-Monitoring of Blood GlucoseDiabetes Care, 1987