Non-Invasive Analyte Testing Using Segmented Spectral Data
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Solution Overview
Problem
Existing non-invasive blood glucose testing methods using spectral data are inaccurate due to interference from skin and subcutaneous tissue spectral data, affecting the accuracy of the output results.
Innovation Solution
A method and system utilizing a convolutional neural network model that processes spectral data from distinct areas of the skin, including testing and reference points, to accurately determine analyte concentration by analyzing uneven distribution of reflection or excitation signals generated by the analyte, using infrared and ultraviolet light to distinguish between venous and non-venous blood vessels.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If spectral analysis technology is used to perform infrared light scanning on a part of a human body to obtain blood glucose concentration, then non-invasive testing is achieved, but the accuracy of the output result is affected due to interference from skin and subcutaneous tissue spectral data
Solution Approach 1:
The patent divides the skin area into multiple regions (testing point and reference point) and processes spectral data from each region separately. By segmenting the spectral data into blood vessel components and surrounding tissue components, the method isolates the analyte signal from interfering signals, thereby improving measurement accuracy while maintaining non-invasive testing.
Solution Approach 2:
The patent extracts and removes interfering spectral data components from the total spectral signal. By identifying and separating the spectral contributions from skin and subcutaneous tissues, the method isolates the pure analyte signal, resolving the contradiction between non-invasive measurement and measurement accuracy.
2Device complexity
If traditional model collects and analyzes only spectral data of an area at which the blood vessel is located, then the testing process is simplified, but the spectral data contains objects such as skin and subcutaneous tissue that also generate spectral data, affecting the accuracy of the output result
Solution Approach 1:
The patent segments the spectral data into distinct components corresponding to different tissue types (blood vessel, skin, subcutaneous tissue). This segmentation allows the model to process each component separately, improving accuracy without significantly increasing overall system complexity.
Solution Approach 2:
The patent applies different processing methods to different regions of the spectral data. By recognizing that different tissue types have different spectral characteristics, the method applies localized analysis strategies to each tissue type, improving overall measurement accuracy while maintaining reasonable computational complexity.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
The method achieves high accuracy in non-invasive blood glucose testing by distinguishing between spectral data from blood vessels and surrounding tissues, reducing interference and improving the reliability of glucose concentration predictions.
Implementation Method 1
spectral data indicating uneven distribution of a reflection signal or an excitation signal generated by the analyte when irradiated by light
Implementation Method 2
based on grayscale distribution of pixels collected from a first image of an imaging area in an area in which a target is irradiated by infrared light
Implementation Method 3
based on grayscale values of pixels collected from a second image of the imaging area at the same area in which the target is irradiated by ultraviolet light
Data Source
AI summary
The present invention relates to the field of optical analysis, and provides a method and a system for testing an analyte based on a testing model, a medium, and a device. The method includes: model input: obtaining an analyte testing model and providing an input amount to the analyte testing model; and model output: collecting an output amount of the analyte testing model, where the input amount includes: spectral data that indicate uneven distribution of a reflection signal or an excitation signal generated by the analyte when irradiated by light; and the output amount includes: a test result of the analyte, and the test result of the analyte includes information of the analyte correlated to the spectral data. In the present invention, the testing model is used to process input spectral data with interference to obtain an accurate output result, thereby improving the output accuracy of the model.