Universal blood glucose prediction method based on frequency band separation and data modeling

Abstract

The invention discloses a universal blood glucose prediction method based on frequency band separation and data modeling, which comprises the steps that a human body subcutaneous blood glucose measurement signal is analyzed; the latent timing sequence dynamic characteristic of the human body subcutaneous blood glucose measurement signal is extracted; a frequency band separation threshold is defined; the subcutaneous blood glucose measurement signal is divided into a high frequency band and a low frequency band; timing sequence autocorrelation of a low frequency blood glucose signal is analyzed; and an autoegression blood glucose prediction model is established. According to the universal blood glucose prediction method, re-modeling for a new object is not required after the sufficient blood glucose measurement signals are acquired, and real-time blood glucose prediction can be performed by directly calling a prediction model of other individual, so that the modeling working capacity and the complexity are simplified greatly; the modeling cost can be lowered greatly; the prediction precision is improved due to the fact that a universal model adopts a method based on frequency band separation and latent variable modeling; and the universal blood glucose prediction method is easy to implement, and indicates a new direction for research of a blood glucose prediction modeling method.

Description

technical field [0001] The invention belongs to the field of blood sugar data analysis and prediction research, in particular to a general blood sugar prediction method based on frequency band separation and data modeling. Background technique [0002] A notable characteristic of human blood glucose level is time-varying, which is specifically reflected in the significant autocorrelation relationship between time-series signal measurements. By analyzing and modeling blood glucose signals, its time-series correlation characteristics can be extracted, and future blood glucose changes can be obtained according to historical blood glucose dynamics. In 1999, foreign scholars Bremer and Gough proposed for the first time that blood glucose time-series data has a potential correlation structure, which can be described by a simple linear dynamic model. At present, the establishment of blood glucose prediction models mostly adopts data-driven methods. Existing forecasting models can...

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Problems solved by technology

[0003] The purpose of the present invention is to provide a general blood sugar prediction method based ...

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