Assessment of linear regression techniques for modeling multisensor data for non-invasive continuous glucose monitoring

Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS

Volumn , Issue , 2011, Pages 2538-2541

  Zanon, Mattia ^a   Riz, Michela ^a   Sparacino, Giovanni ^a   Facchinetti, Andrea ^a   Suri, Roland E ^b   Talary, Mark S ^b   Cobelli, Claudio ^a  

^a UNIVERSITY OF PADOVA   (Italy)

^b Solianis Monitoring AG   (Switzerland)

Author keywords

[No Author keywords available]

Indexed keywords

CONTINUOUS GLUCOSE MONITORING; GLUCOSE CONCENTRATION; GLUCOSE LEVEL; LEAST ABSOLUTE SHRINKAGE AND SELECTION OPERATORS; LINEAR REGRESSION TECHNIQUES; MODEL IDENTIFICATION; MULTI SENSOR; MULTI-SENSOR DATA; MULTIVARIATE LINEAR REGRESSION MODEL; NON-INVASIVE; ORDINARY LEAST SQUARES; PARTIAL LEAST SQUARES; SWITZERLAND; TRAINING SETS;

LINEAR REGRESSION; MATHEMATICAL MODELS; SENSORS;

GLUCOSE;

ARTICLE; BLOOD; GENETIC PROCEDURES; GLUCOSE BLOOD LEVEL; HUMAN; INSULIN DEPENDENT DIABETES MELLITUS; STATISTICAL MODEL; THEORETICAL MODEL; VALIDATION STUDY;

BIOSENSING TECHNIQUES; BLOOD GLUCOSE; DIABETES MELLITUS, TYPE 1; HUMANS; LINEAR MODELS; MODELS, THEORETICAL;

EID: 84862617923     PISSN: 1557170X     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/IEMBS.2011.6090702     Document Type: Conference Paper

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