Noninvasive blood sugar data processing method and noninvasive blood sugar data processing system based on convolutional neural network

Abstract

The invention discloses a noninvasive blood sugar data processing method and a noninvasive blood sugar data processing system based on a convolutional neural network. The method comprises the steps of acquiring a plurality of sets of blood sugar data; performing calculation for acquiring a maximum infrared signal; through singular spectrum analysis and empirical mode decomposition, performing decomposition, grouping and ordering on the maximum infrared signal; respectively extracting the maximum infrared signal, the average value, the variance, the slope and the peak value of front N sets of component data, thereby constructing a characteristic signal; according to the characteristic signal and the blood sugar value of a plurality of sets of blood sugar data, constructing a mapping matrix; according to a to-be-tested signal of a to-be-tested person and the mapping matrix, constructing a to-be-tested mapping matrix; by means of a characteristic mapping layer and a pooling layer of the convolutional neural network, optimizing the to-be-tested mapping matrix, and outputting an optimization result, wherein a radial primary function is used as an activating function in a characteristic mapping layer; and the pooling layer is used for reducing the number of dimensions of the signal. The noninvasive blood sugar data processing method and the noninvasive blood sugar data processing system can improve blood sugar data estimation precision.

Description

technical field [0001] The present invention relates to the field of computer technology, in particular to a convolutional neural network-based non-invasive blood glucose data processing method and system. Background technique [0002] Diabetes is a chronic killer that seriously endangers human health, and there are nearly 50 million potential early diabetic patients. If diabetics can regularly detect blood sugar, it is of great significance to control the amount of sugar in the body. Most of the traditional diabetes self-testing devices are invasive blood collection, and need to be used with disposable test strips, which are expensive for long-term use. With the development of science and technology, non-invasive blood glucose detectors are gradually becoming popular. The basic principle is to estimate the corresponding blood glucose concentration through a preset algorithm after collecting the information of human signs through infrared. [0003] Existing blood glucose d...

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Problems solved by technology

[0003] Existing blood glucose data processing methods include evaluating blood glucose concentration based on fractional differential equations or Volterra series, but because fractional differential equations belong to linear systems, and blood glucose signals have nonlinear characteristics, th

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