Article

# The importance of different frequency bands in predicting subcutaneous glucose concentration in type 1 diabetic patients.

Bioinformatics Cell, Telemedicine and Advanced Technology Research Center, U.S. Army Medical Research and Materiel Command, Fort Detrick, MD 21702, USA.

IEEE transactions on bio-medical engineering (Impact Factor: 2.15). 08/2010; 57(8):1839-46. DOI: 10.1109/TBME.2010.2047504 Source: PubMed

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**ABSTRACT:**Previously, our group developed auto-regressive (AR) models to predict human core temperature and help prevent hyperthermia (temperature > 39 oC). However, the models often yielded delayed predictions, limiting their application as a real-time warning system. To mitigate this problem, here we combined AR-model point estimates with statistically derived prediction intervals (PIs) and assessed the performance of three new alert algorithms [AR model plus PI, median filter of AR model plus PI decisions, and an adaptation of the sequential probability ratio test (SPRT)]. Using field-study data from 22 Soldiers, including five subjects who experienced hyperthermia, we assessed the alert algorithms for AR-model prediction windows from 15-30 min. Cross-validation simulations showed that, as the prediction windows increased, improvements in the algorithms' effective prediction horizons were offset by deteriorating accuracy, with a 20-min window providing a reasonable compromise. Model plus PI and SPRT yielded the largest effective prediction horizons (≥ 18 min), but these were offset by other performance measures. If high sensitivity and a long effective prediction horizon are desired, model plus PI provides the best choice, assuming decision switches can be tolerated. In contrast, if a small number of decision switches are desired, SPRT provides the best compromise as an early warning system of impending heat illnesses.IEEE Journal of Biomedical and Health Informatics 06/2014; · 1.98 Impact Factor - [Show abstract] [Hide abstract]

**ABSTRACT:**Online glucose prediction which can be used to provide important information of future glucose status is a key step to facilitate proactive management before glucose reaches undesirable concentrations. Based on frequency‐band separation (FS) and empirical modeling approaches, this article considers several important aspects of on‐line glucose prediction for subjects with type 1 diabetes mellitus. Three issues are of particular interest: (1) Can a global (or universal) model be developed from glucose data for a single subject and then used to make suitably accurate on‐line glucose predictions for other subjects? (2) Does a new FS approach based on data filtering provide more accurate models than standard modeling methods? (3) Does a new latent variable modeling method result in more accurate models than standard modeling methods? These and related issues are investigated by developing autoregressive models and autoregressive models with exogenous inputs based on clinical data for two groups of subjects. The alternative modeling approaches are evaluated with respect to on‐line short‐term prediction accuracy for prediction horizons of 30 and 60 min, using independent test data. © 2013 American Institute of Chemical Engineers AIChE J 60: 574–584, 2014AIChE Journal 02/2014; 60(2). · 2.58 Impact Factor -
##### Conference Paper: Multivariate statistical analysis methods to investigate interindividual glucose dynamics for subjects with type 1 diabetes mellitus

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**ABSTRACT:**This paper investigates the interindividual variability of underlying glucose dynamics using multivariate statistical analysis methods for subjects with type 1 diabetes mellitus. Here two types of glucose dynamics are defined, the general dynamics and the output-relevant predictive dynamics. The concerned important issues are whether the underlying glucose dynamics change from subject to subject? Can a global (or universal) empirical model be developed from glucose data for a single subject and then used to explain the glucose dynamics for other subjects? These and related issues are investigated using multivariate statistical analysis methods based on clinical data for two groups of subjects. Together, these findings provide insights into more efficient development of data-driven models to understand and capture the glucose information in diabetes subjects.Intelligent Control and Automation (WCICA), 2012 10th World Congress on; 01/2012

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