Measures from Nonlinear Dynamics Reflect Glucose Current Sensor Degradation
Advancements in continuous glucose monitoring technology has enabled development of closed-loop insulin-glucagon delivery systems. Monitoring the reliability and fidelity of glucose current, \(\varSigma I_g(t)\), becomes essential for the safety of patients utilizing these closed loop systems (Barnaba et al., Diab Technol Ther 5:27–31, 2005) . Because time series of \(\varSigma I_g(t)\) evidence chaotic nonlinear hyperbolic (expanding and mixing) dynamical behavior (Ruelle and Takens, Commun Math Phys 20(3):167–192, 1971) , we use the complexity measures from dynamical measure theory to discriminate normal function from progressive dysfunction in glucose sensors (Cornfeld et al., Ergodic Theory, 2012) . We present a method of characterizing the \(\varSigma I_g(t)\) from the continuous glucose monitor signal, CGM, using a set of entropy equivalent information measures (EEIM) that, when combined with the use of a support vector machine, were found to distinguish between functional and failing continuous glucose sensors.
KeywordsSupport Vector Machine Continuous Glucose Monitoring Hurst Exponent Commun Math Phys Continuous Glucose Monitor
This work was funded by the Juvenile Diabetes Research Foundation and John Fetzer Memorial Trust.
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