Nonparametric Identification of Glucose-Insulin Process in IDDM Patient with Multi-meal Disturbance
- PDF / 428,128 Bytes
- 10 Pages / 595.276 x 790.866 pts Page_size
- 79 Downloads / 196 Views
IGINAL CONTRIBUTION
Nonparametric Identification of Glucose-Insulin Process in IDDM Patient with Multi-meal Disturbance A. Bhattacharjee • A. Sutradhar
Received: 30 August 2012 / Accepted: 11 January 2013 / Published online: 18 April 2013 Ó The Institution of Engineers (India) 2013
Abstract Modern close 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 frequency domain nonparametric identification of the nonlinear glucose-insulin process in an insulin dependent diabetes mellitus patient that captures the process dynamics in presence of uncertainties and parameter variations. An online frequency domain kernel estimation method has been proposed that uses the input–output data from the 19th order first principle model of the patient in intravenous route. Volterra equations up to second order kernels with extended input vector for a Hammerstein model are solved online by adaptive recursive least square (ARLS) algorithm. The frequency domain kernels are estimated using the harmonic excitation input data sequence from the virtual patient model. A short filter memory length of M = 2 was found sufficient to yield acceptable accuracy with lesser computation time. The nonparametric models are useful for closed loop control, where the frequency domain kernels can be directly used as the transfer function. The validation results show good fit both in frequency and time domain responses with nominal patient as well as with parameter variations.
A. Bhattacharjee (&) Department of Electrical Engineering, Bengal Engineering & Science University, Shibpur, Howrah 711103, India e-mail: [email protected] A. Sutradhar Centre for Healthcare Science and Technology, Bengal Engineering & Science University, Shibpur, Howrah 711103, India
Keywords System identification Nonparametric model Glucose-insulin interaction Hammerstein model Frequency domain kernels
Introduction According to diabetes control and complications trial (DCCT) [1], long-term complications associated with diabetes can be avoided to a large extent by maintaining normoglycaemia (i.e. arterial glucose level not exceeding 120 mg/dL). In modern fast lifestyle pattern, intensive treatment of type-1 or insulin dependent diabetes mellitus (IDDM) patients requires continuous and controlled release of insulin to the bloodstream to regulate the level of blood glucose (BG) in presence of normal meal and activity conditions [2–8]. Effective control of biological processes is a non-trivial task requiring a model that is able to adequately acquire the dynamic behaviour of the process over its complete operating range. The complex nonlinear process of glucose metabolism is linked to a number of internal factors. With occasional blood glucose sensing, routine food intake and other activity conditions, the system appears highly stochastic. The closed
Data Loading...
