Your browser doesn't support javascript.

Biblioteca Virtual em Saúde


Home > Pesquisa > ()
Imprimir Exportar

Formato de exportação:


Adicionar mais destinatários
| |

Adaptive System Identification for Estimating Future Glucose Concentrations and Hypoglycemia Alarms.

Eren-Oruklu, Meriyan; Cinar, Ali; Rollins, Derrick K; Quinn, Lauretta.
Automatica (Oxf); 48(8): 1892-1897, 2012 Aug.
Artigo em Inglês | MEDLINE | ID: mdl-22865931
Many patients with diabetes experience high variability in glucose concentrations that includes prolonged hyperglycemia or hypoglycemia. Models predicting a subject's future glucose concentrations can be used for preventing such conditions by providing early alarms. This paper presents a time-series model that captures dynamical changes in the glucose metabolism. Adaptive system identification is proposed to estimate model parameters which enable the adaptation of the model to inter-/intra-subject variation and glycemic disturbances. It consists of online parameter identification using the weighted recursive least squares method and a change detection strategy that monitors variation in model parameters. Univariate models developed from a subject's continuous glucose measurements are compared to multivariate models that are enhanced with continuous metabolic, physical activity and lifestyle information from a multi-sensor body monitor. A real life application for the proposed algorithm is demonstrated on early (30 min in advance) hypoglycemia detection.
Selo DaSilva