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Automatic detection of stress states in type 1 diabetes subjects in ambulatory conditions / Daniel A. Finan in Industrial & engineering chemistry research, Vol. 49 N° 17 (Septembre 1, 2010) 

Public ISBD [article]  in Industrial & engineering chemistry research > Vol. 49 N° 17 (Septembre 1, 2010) . - pp 7843–7848 Titre : Automatic detection of stress states in type 1 diabetes subjects in ambulatory conditions Type de document : texte imprimé Auteurs : Daniel A. Finan, Auteur ; Howard Zisser, Auteur ; Lois Jovanovic, Auteur Année de publication : 2010 Article en page(s) : pp 7843–7848 Note générale : Chimie industrielle Langues : Anglais (eng) Mots-clés : Automatic  detection  Ambulatory  conditions. Résumé : Two levels of control are crucial to the robustness of an artificial β-cell, a medical device that would automatically regulate blood glucose levels in patients with type 1 diabetes. A low-level component would attempt to regulate blood glucose continuously, whereas a supervisory-level, or monitoring, component would detect underlying changes in the subject’s glucose−insulin dynamics and take corrective actions accordingly. These underlying changes, or “faults”, can include changes in insulin sensitivity, sensor problems, and insulin delivery problems, to name a few. A multivariate statistical monitoring technique, principal component analysis (PCA), has been applied to both simulated and experimental type 1 diabetes data. The objective of this study was to determine if PCA could be used to distinguish between normal patient data and data for abnormal conditions that included a variety of “faults.” The PCA results showed a high degree of accuracy; for data from nine type 1 diabetes subjects under ambulatory conditions, 33 of 37 total test days (89%), including fault days and normal days, were classified correctly. Therefore, the proposed monitoring technique shows considerable promise for incorporation into an artificial β-cell. DEWEY : 660 ISSN : 0888-5885 En ligne : http://pubs.acs.org/doi/abs/10.1021/ie901891c [article] Automatic detection of stress states in type 1 diabetes subjects in ambulatory conditions [texte imprimé] / Daniel A. Finan, Auteur ; Howard Zisser, Auteur ; Lois Jovanovic, Auteur . - 2010 . - pp 7843–7848. Chimie industrielle Langues : Anglais (eng) in Industrial & engineering chemistry research > Vol. 49 N° 17 (Septembre 1, 2010) . - pp 7843–7848 Mots-clés : Automatic  detection  Ambulatory  conditions. Résumé : Two levels of control are crucial to the robustness of an artificial β-cell, a medical device that would automatically regulate blood glucose levels in patients with type 1 diabetes. A low-level component would attempt to regulate blood glucose continuously, whereas a supervisory-level, or monitoring, component would detect underlying changes in the subject’s glucose−insulin dynamics and take corrective actions accordingly. These underlying changes, or “faults”, can include changes in insulin sensitivity, sensor problems, and insulin delivery problems, to name a few. A multivariate statistical monitoring technique, principal component analysis (PCA), has been applied to both simulated and experimental type 1 diabetes data. The objective of this study was to determine if PCA could be used to distinguish between normal patient data and data for abnormal conditions that included a variety of “faults.” The PCA results showed a high degree of accuracy; for data from nine type 1 diabetes subjects under ambulatory conditions, 33 of 37 total test days (89%), including fault days and normal days, were classified correctly. Therefore, the proposed monitoring technique shows considerable promise for incorporation into an artificial β-cell. DEWEY : 660 ISSN : 0888-5885 En ligne : http://pubs.acs.org/doi/abs/10.1021/ie901891c