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Résumé :
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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.
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