Measures from Nonlinear Dynamics Reflect Glucose Current Sensor Degradation

  • Eric MauritzenEmail author
  • Arnold Mandell
  • David Tallman
  • Bruce Buckingham
Conference paper
Part of the Lecture Notes in Networks and Systems book series (LNNS, volume 6)


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) [2]. 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) [10], 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) [3]. 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.


Support Vector Machine Continuous Glucose Monitoring Hurst Exponent Commun Math Phys Continuous Glucose Monitor 
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This work was funded by the Juvenile Diabetes Research Foundation and John Fetzer Memorial Trust.


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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Eric Mauritzen
    • 1
    Email author
  • Arnold Mandell
    • 1
  • David Tallman
    • 1
  • Bruce Buckingham
    • 2
  1. 1.University of California San DiegoSan DiegoUSA
  2. 2.Department of Pediatric EndocrinologyStanford UniversityStanfordUSA

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