• DocumentCode
    1400823
  • Title

    PSECMAC Intelligent Insulin Schedule for Diabetic Blood Glucose Management Under Nonmeal Announcement

  • Author

    Teddy, S.D. ; Quek, C. ; Lai, E.M.-K. ; Cinar, A.

  • Author_Institution
    Data Min. Dept., A*STAR, Singapore, Singapore
  • Volume
    21
  • Issue
    3
  • fYear
    2010
  • fDate
    3/1/2010 12:00:00 AM
  • Firstpage
    361
  • Lastpage
    380
  • Abstract
    Therapeutically, the closed-loop blood glucose-insulin regulation paradigm via a controllable insulin pump offers a potential solution to the management of diabetes. However, the development of such a closed-loop regulatory system to date has been hampered by two main issues: 1) the limited knowledge on the complex human physiological process of glucose-insulin metabolism that prevents a precise modeling of the biological blood glucose control loop; and 2) the vast metabolic biodiversity of the diabetic population due to varying exogneous and endogenous disturbances such as food intake, exercise, stress, and hormonal factors, etc. In addition, current attempts of closed-loop glucose regulatory techniques generally require some form of prior meal announcement and this constitutes a severe limitation to the applicability of such systems. In this paper, we present a novel intelligent insulin schedule based on the pseudo self-evolving cerebellar model articulation controller (PSECMAC) associative learning memory model that emulates the healthy human insulin response to food ingestion. The proposed PSECMAC intelligent insulin schedule requires no prior meal announcement and delivers the necessary insulin dosage based only on the observed blood glucose fluctuations. Using a simulated healthy subject, the proposed PSECMAC insulin schedule is demonstrated to be able to accurately capture the complex human glucose-insulin dynamics and robustly addresses the intraperson metabolic variability. Subsequently, the PSECMAC intelligent insulin schedule is employed on a group of type-1 diabetic patients to regulate their impaired blood glucose levels. Preliminary simulation results are highly encouraging. The work reported in this paper represents a major paradigm shift in the management of diabetes where patient compliance is poor and the need for prior meal announcement under current treatment regimes poses a significant challenge to an active lifestyle.
  • Keywords
    biochemistry; blood; cerebellar model arithmetic computers; diseases; learning (artificial intelligence); medical control systems; patient treatment; PSECMAC intelligent insulin schedule; associative learning memory model; closed-loop blood glucose-insulin regulation; controllable insulin pump; diabetic blood glucose management; food ingestion; glucose-insulin metabolism; metabolic biodiversity; nonmeal announcement; pseudo self-evolving cerebellar model articulation controller; Diabetes; insulin therapy; intelligent insulin schedule; no meal announcement; pseudo self-evolving cerebellar model articulation controller (PSECMAC); Adult; Artificial Intelligence; Association Learning; Blood Glucose; Drug Administration Schedule; Drug Delivery Systems; Eating; Humans; Hypoglycemic Agents; Insulin; Male; Memory; Models, Biological; Time Factors;
  • fLanguage
    English
  • Journal_Title
    Neural Networks, IEEE Transactions on
  • Publisher
    ieee
  • ISSN
    1045-9227
  • Type

    jour

  • DOI
    10.1109/TNN.2009.2036726
  • Filename
    5404263