Non-invasive Blood Glucose Algorithm Using Infrared Spectral Calibration
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Solution Overview
Problem
Current non-invasive methods for monitoring blood glucose levels are unreliable due to instrumental drift, temperature changes, and spectral irregularities, which affect the quality of calibration models and algorithms used to determine analyte concentrations in body fluids.
Innovation Solution
A system comprising an infrared light source, a body tissue interface, and a detector, with a central processing unit that compares spectral information to an algorithm to accurately determine analyte concentrations in body fluids, accounting for tissue properties and irregularities through calibration algorithms built using data from glucose clamping tests.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Object-affected harmful factors
If non-invasive spectral analysis is used to monitor blood glucose levels, then pain and skin laceration are eliminated, but measurement reliability deteriorates due to instrumental drift, temperature changes, and spectral irregularities
Solution Approach 1:
The system performs preliminary calibration by measuring spectral data from multiple subjects during glucose clamping tests before actual use. This pre-established calibration data accounts for various tissue properties and spectral irregularities, enabling reliable measurements without repeated invasive procedures
Solution Approach 2:
The system continuously monitors spectral data and compares it against the calibration model to detect deviations caused by instrumental drift, temperature changes, or tissue variability. The algorithm adjusts measurements based on this feedback to maintain accuracy over time
2Ease of operation
If spectral data is collected from body tissue, then blood glucose concentration can be determined non-invasively, but spectral irregularities from tissue properties and environmental factors reduce measurement accuracy
Solution Approach 1:
Calibration data is collected in advance from multiple subjects undergoing glucose clamping tests, capturing the full range of tissue properties and spectral irregularities. This pre-established reference model enables accurate glucose determination without requiring perfect spectral data during actual measurements
Solution Approach 2:
The calibration algorithm accounts for variations in tissue properties (water content, fat content, blood flow) and environmental conditions (temperature, pressure) by incorporating these parameters into the spectral analysis model, allowing accurate glucose measurement despite spectral irregularities
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
Enables reliable, pain-free, and accurate non-invasive monitoring of blood glucose levels by accounting for tissue properties and irregularities, improving the accuracy of glucose concentration predictions.
Implementation Method 1
an infrared light source, a body tissue interface, a detector... deliver light from the infrared light source to the contacted body tissue
Implementation Method 2
The detector is adapted to receive spectral information corresponding to infrared light transmitted through the portion of body tissue being analyzed and to convert the received spectral information into an electrical signal
Data Source
AI summary
A system for determining the concentration of an analyte in at least one body fluid in body tissue comprises an infrared light source, a body tissue interface, a detector, and a central processing unit. The body tissue interface is adapted to contact body tissue and to deliver light from the infrared light source to the contacted body tissue. The detector is adapted to receive spectral information corresponding to infrared light transmitted through the portion of body tissue being analyzed and to convert the received spectral information into an electrical signal indicative of the received spectral information. The central processing unit is adapted to compare the electrical signal to an algorithm built upon correlation with the analyte in body fluid, the algorithm adapted to convert the received spectral information into the concentration of the analyte in at least one body fluid.