COMBINED PROCESSING OF BLOOD GLUCOSE SELF-MONITORING
Background. The self-monitoring of Blood Glucose for a long time is a healthcare tool. The records study allows a correcting of a lifestyle and a cure for people with diabetes. The trend, seasonality, and variability of series are basic for such decisions. A combined processing is offered. It uses the Singular Spectrum Analysis (SSA), using Poincare Plot (PP) as well as the statistical autocorrelation function (ACF) studies.
Materials and methods. The patient (n=1) is a 67-year-old male with the long history of type 2 diabetes (T2D). Tests were carried out three times a week. The scheme: "Monday-Wednesday-Friday" was in use. The total time was 176 weeks, so the time series has the length of N=528 samples.
Results. The ACF has forecasted the series seasonality at first. A sum of the smooth trend, the slowest oscillations and the residuals (noises) was a model of the series. Such an approach is a standard of SSA. We found the smooth trend with a change-point. Slowest period, alias the seasonality, was close to six months. PP analysis shows the random nature of the short-term variability of series while the long-term one has the seasonality as the main source.
Conclusions. The trend has had a convex shape. We found a delay between the starting point for the insulin cure and the change-point. This delay was close to the period of slowest oscillations (about six months). The results of the SSA, PP analysis, and the ACF forecasts were consistent.
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