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. 2013 Jan 1;7(1):206-14.
doi: 10.1177/193229681300700126.

Hypoglycemia early alarm systems based on recursive autoregressive partial least squares models

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

Hypoglycemia early alarm systems based on recursive autoregressive partial least squares models

Elif Seyma Bayrak et al. J Diabetes Sci Technol. .

Abstract

Background: Hypoglycemia caused by intensive insulin therapy is a major challenge for artificial pancreas systems. Early detection and prevention of potential hypoglycemia are essential for the acceptance of fully automated artificial pancreas systems. Many of the proposed alarm systems are based on interpretation of recent values or trends in glucose values. In the present study, subject-specific linear models are introduced to capture glucose variations and predict future blood glucose concentrations. These models can be used in early alarm systems of potential hypoglycemia.

Method: A recursive autoregressive partial least squares (RARPLS) algorithm is used to model the continuous glucose monitoring sensor data and predict future glucose concentrations for use in hypoglycemia alarm systems. The partial least squares models constructed are updated recursively at each sampling step with a moving window. An early hypoglycemia alarm algorithm using these models is proposed and evaluated.

Results: Glucose prediction models based on real-time filtered data has a root mean squared error of 7.79 and a sum of squares of glucose prediction error of 7.35% for six-step-ahead (30 min) glucose predictions. The early alarm systems based on RARPLS shows good performance. A sensitivity of 86% and a false alarm rate of 0.42 false positive/day are obtained for the early alarm system based on six-step-ahead predicted glucose values with an average early detection time of 25.25 min.

Conclusions: The RARPLS models developed provide satisfactory glucose prediction with relatively smaller error than other proposed algorithms and are good candidates to forecast and warn about potential hypoglycemia unless preventive action is taken far in advance.

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Figures

Figure 1
Figure 1
Early and immediate hypoglycemia detection algorithm flow chart. BG, blood glucose.
Figure 2
Figure 2
Raw CGMS data (blue line) is filtered to eliminate noise in the signal. SG, sensor glucose.
Figure 3
Figure 3
Early hypoglycemia alarms are issued, on average, 25 min in advance of a hypoglycemia event. PLSR, partial least squares regression; BG, blood glucose.
See this image and copyright information in PMC

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