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Abstract:
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Continuous monitoring of physiological signals leads us to a new era, high-throughput phenotyping era. High-throughput phenotyping generates a huge amount of data. One example of high-throughput phenotyping technologies is continuous glucose monitoring (CGM) for diabetes care. One of the major barriers to uptake include uncertainty on how best to use CGM data to make therapeutic decision. Currently in clinical practice, the use of CGM data is often limited to summary statistics like average values or range of blood glucose as well as some calculation of the percentage of time spent above and below given thresholds. Recent development on complexity and fractality may bring new insights to improve the use of CGM data for clinical decision. Recently, we have not only developed a R package CGManalyzer implementing complexity analysis and other methods to analyze CGM data but also apply these methods to analyze CGM data in clinical studies. In this presentation, I will introduce complexity and fractality analysis for glucose dynamics and present their application to diabetes diagnosis and treatment.
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