Conference Proceedings

International Conference on Advances In Computing, Electronics and Electrical Technology CEET 2014

Data Driven Identification of IDDM Patient Model

Author(s) : ARPITA BHATTACHARJEE, ASHOKE SUTRADHAR

Abstract

Prerequisite to the better living of an insulin dependent diabetes mellitus (IDDM) or type-1 diabetic patients is the closed loop blood glucose regulation via subcutaneous insulin infusion and continuous glucose monitoring system (SC-SC route). Closed loop control for blood glucose level in a diabetic patient necessarily uses an explicit model of the process. A fixed parameter full order or reduced order model does not characterize the inter-patient and intra-patient parameter variability. This paper deals with a real time implementation of online identification of frequency domain kernels from the input output data of an IDDM patient. The data-driven model of the patient is identified in real time by solving Volterra kernels up to second order using adaptive recursive least square (ARLS) algorithm with a short memory length of M=2. The frequency domain kernels, or the Volterra transfer function (VTF) are computed by taking the FFTs on respective time domain kernels for a specific length of extended input vector. The dynamic glucose-insulin process model of a IDDM patient in SC-SC route based on the work of Dalla Man et. al. has been constructed in hardware platform that acts as a virtual patient. The validation results have shown good fit of responses with nominal patient in simulation as well as with online identification.

Conference Title : International Conference on Advances In Computing, Electronics and Electrical Technology CEET 2014
Conference Date(s) : 02 - 03 August, 2014
Place : Hotel G Tower, Kuala Lumpur, Malaysia
No fo Author(s) : 2
DOI : 10.15224/978-1-63248-005-7-43
Page(s) : 94 - 98
Electronic ISBN : 978-1-63248-005-7
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