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. 2013 Sep 4;52(35):12329-12336.
doi: 10.1021/ie3034015.

Hypoglycemia Early Alarm Systems Based On Multivariable Models

Kamuran Turksoy  1 , Elif S Bayrak, Lauretta Quinn, Elizabeth Littlejohn, Derrick Rollins, Ali Cinar
Affiliations

Hypoglycemia Early Alarm Systems Based On Multivariable Models

Kamuran Turksoy et al. Ind Eng Chem Res. .

Abstract

Hypoglycemia is a major challenge of artificial pancreas systems and a source of concern for potential users and parents of young children with Type 1 diabetes (T1D). Early alarms to warn the potential of hypoglycemia are essential and should provide enough time to take action to avoid hypoglycemia. Many alarm systems proposed in the literature are based on interpretation of recent trends in glucose values. In the present study, subject-specific recursive linear time series models are introduced as a better alternative to capture glucose variations and predict future blood glucose concentrations. These models are then used in hypoglycemia early alarm systems that notify patients to take action to prevent hypoglycemia before it happens. The models developed and the hypoglycemia alarm system are tested retrospectively using T1D subject data. A Savitzky-Golay filter and a Kalman filter are used to reduce noise in patient data. The hypoglycemia alarm algorithm is developed by using predictions of future glucose concentrations from recursive models. The modeling algorithm enables the dynamic adaptation of models to inter-/intra-subject variation and glycemic disturbances and provides satisfactory glucose concentration prediction with relatively small error. The alarm systems demonstrate good performance in prediction of hypoglycemia and ultimately in prevention of its occurrence.

Keywords: Hypoglycemia alarms; diabetes; multivariable models; process monitoring; recursive algorithms.

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Figures

Figure 1
Figure 1
Early hypoglycemia alarm performance for 30 min predictions for subject 1
Figure 2
Figure 2
Early hypoglycemia alarm flow chart for real time implementation
See this image and copyright information in PMC

References

    1. Centers for Disease; Control; Prevention. National Diabetes Fact Sheet: National Estimates and General Information on Diabetes and Prediabetes in the United States, 2011. US Department of Health and Human Services; 2011.
    1. Buckingham B, Chase HP, Dassau E, Cobry E, Clinton P, Gage V, Caswell K, Wilkinson J, Cameron F, Lee H, Bequette BW, Doyle FJ., III Prevention of Nocturnal Hypoglycemia Using Predictive Alarm Algorithms and Insulin Pump Suspension. Diabetes Care. 2010;33(5):1013–7. - PMC - PubMed
    1. Buckingham B, Cobry E, Clinton P, Gage V, Caswell K, Kunselman E, Cameron F, Chase HP. Preventing Hypoglycemia Using Predictive Alarm Algorithms and Insulin Pump Suspension. Diabetes Technol Ther. 2009;11(2):93–97. - PMC - PubMed
    1. Agrawal P, Welsh JB, Kannard B, Askari S, Yang Q, Kaufman FR. Usage and Effectiveness of the Low Glucose Suspend Feature of the Medtronic Paradigm Veo Insulin Pump. J Diabetes Sci Technol. 2011;5(5):1137–41. - PMC - PubMed
    1. Cameron F, Wilson DM, Buckingham BA, Arzumanyan H, Clinton P, Chase HP, Lum J, Maahs DM, Calhoun PM, Bequette BW. Inpatient Studies of a Kalman-Filter-Based Predictive Pump Shutoff Algorithm. J Diabetes Sci Technol. 2012;6(5):1142–7. - PMC - PubMed