Using Distributional Data Analysis Tool to Investigate the Sensor Collected Glucose Density (GD) Distribution of the Daily Mean Glucose (eAG), Fasting Plasma Glucose (FPG), and Postprandial Plasma Glucose (PPG) Collected from a Continuous Glucose Monitoring Sensor Device of a Long-Term Type 2 Diabetes Patient Over a Period of 3.33 Years Based on GH-Method: Math-Physical Medicine (No. 509)

Abstract

Gerald C Hsu

Recently, the author have made two further improvements on his glucose data analysis with his collected big data of sensor glucoses via a continuous glucose monitoring sensor device (CGM). First, in addition to using the HbA1C, which is the mean value of the past 115 days of red blood cell’s glucoses, of a patient as the golden standard in evaluating diabetes conditions. He investigates the glucose fluctuation or GF (glucose excursion or glycemic variability) and then transforms the GF values from a wave’s time-space into an energy’s frequency-space via Fourier transform operations. Using this analytic approach, he can then guesstimate the degree of damage caused on internal organs by the energies associated with various glucose fluctuations. Although the GF research is one step deeper compared to the study of mean value of glucoses, such as HbA1C, it is still not deep enough in order to dig out more detailed and useful information hidden inside of the glucose waves.

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