Continuous blood glucose monitoring device comprising parameter estimation function filtering module

Abstract

The invention discloses a continuous blood glucose monitoring device comprising a parameter estimation function filtering module. The continuous blood glucose monitoring device comprising the parameter estimation function filtering module can filter continuous blood glucose monitored signals to achieve accurate hyperglycemia and hypoglycemia alarming. Under the conditions of differences of different patients and different sensors, the noise levels of corresponding blood glucose monitored signals are different and accordingly need different filter parameters for filtering treatment. Therefore, according to the continuous blood glucose monitoring device comprising the parameter estimation function filtering module, the filter parameters can be adjusted according to the different patients and the different sensors. A filter parameter estimation method of the device can timely and accurately estimate the filter parameters required for Kalman filtering, so that better filtering treatment on the blood glucose signals can be achieved, and a solid foundation for improving the accuracy of real-time hyperglycemia and hypoglycemia alarming can be laid, and high significance can be obtained.

Description

technical field [0001] The invention belongs to the research field of blood sugar data processing and analysis, in particular to a continuous blood sugar monitoring device including a filter module with parameter estimation function. Background technique [0002] In order to manage and monitor blood glucose levels, it is necessary to measure blood glucose levels, currently using continuous glucose monitoring devices. With the rapid development of continuous blood glucose monitoring (CGM) equipment, higher-precision control becomes possible, and the real-time CGM system plays an important role in the early detection of high / low blood sugar. Alarms can be generated by comparing the relationship between the current measured value and the high / low blood sugar threshold, and timely high and low blood sugar alarms, especially at night, are particularly important for diabetics. But generally speaking, CGM data contains noise, and because of this, it will affect the alarm of high a...

AI Technical Summary

Problems solved by technology

In some published patents, the algorithm of moving average filtering is generally used, but the filtering effect of moving average filtering is not as good as that of Kalman filtering.

[0003] However, in the practical application of Kalman filtering, the amount of random noise is not known, and the signal-to-noise ratio will vary with different objects and different sensors (inter-individual differences)

At this time, if the parameters of the Kalman filter cannot follow the change of the signal-to-noise ratio, the filtering effect will be suboptimal.

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