Work

Automatic Detection of Stress States in Type 1 Diabetes Subjects in Ambulatory Conditions

Year: 2010

Type: article

Abstract: 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, while 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 in ambulatory conditions, 33 of 37 total test days (89%), including fault days and normal days, were classified correctly. Thus, the proposed monitoring technique shows considerable promise for incorporation into an artificial β-cell.

Source: Industrial & Engineering Chemistry Research

DOI: 10.1021/ie901891c

Authors: Daniel A. Finan, Howard Zisser, Lois Jovanovič, Wendy C. Bevier, Dale E. Seborg

Institutions: University of California, Santa Barbara, Sansum Diabetes Research Institute

Language: English

Topic: Fault Detection and Control Systems

Subfield: Control and Systems Engineering

Field: Engineering

Domain: Physical Sciences

SDG: Good health and well-being

Open Access status: green

Funder: National Institute of Diabetes and Digestive and Kidney Diseases