Fourier Approximation for Continuous Glucose Monitoring Data Analysis
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Solution Overview
Problem
Continuous glucose monitoring systems (CGMS) data is plagued by variability and noise, making it difficult to accurately aggregate and analyze, which hinders the diagnosis and treatment of hypoglycemia and diabetes, as well as the prediction of insulin therapy effectiveness.
Innovation Solution
Applying a Fourier approximation to smooth out the data, decomposing it into harmonic components, and correlating these components with medication-based therapy recommendations to predict average blood glucose levels and susceptibility to hypoglycemia.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If continuous glucose monitoring is used to obtain detailed blood glucose level data, then measurement precision is improved, but data variability and noise increase
Solution Approach 1:
The patent segments the continuous glucose monitoring data into discrete time intervals (e.g., 5-minute intervals) and applies Fourier approximation to decompose the data into frequency components. This segmentation approach transforms the noisy continuous signal into manageable discrete segments that can be analyzed separately, reducing the impact of random noise while preserving the underlying glucose level patterns.
Solution Approach 2:
The patent introduces Fourier approximation as an intermediary mathematical tool between the raw CGMS data and the clinical interpretation. This intermediary process filters out high-frequency noise components while preserving the lower-frequency physiological glucose patterns, effectively mediating between the noisy measurements and reliable clinical insights.
2Reliability
If Fourier approximation is applied to smooth CGMS data, then data reliability is improved, but device complexity increases
Solution Approach 1:
The patent changes the parameter representation of glucose data from time-domain values to frequency-domain parameters through Fourier approximation. By transforming the data into harmonic components with specific frequencies, amplitudes, and phases, the method achieves smoothing and noise reduction while providing clinically meaningful parameters that can be directly interpreted in terms of glucose patterns and rhythms.
3Measurement precision
If detailed CGMS data is collected without aggregation, then measurement precision is improved, but ease of operation deteriorates
Solution Approach 1:
The patent applies periodic action by using Fourier approximation to identify and analyze periodic patterns in glucose data. The method decomposes the glucose profile into harmonic components that represent regular physiological rhythms, enabling easy aggregation and comparison of data across different patients and time periods by focusing on these fundamental periodic patterns rather than raw individual measurements.
Data Source
AI summary
A method for predicting the effectiveness of medication-based therapy in lowering average blood glucose levels in a diabetic patient is provided. This method may further comprise selectively recommending a medication-based therapy on the basis of the arithmetic average of the relative minima. A method for determining susceptibility to symptomatic hypoglycemia in a patient is provided. This method may further comprise selectively recommending a medication-based therapy on the basis of the arithmetic average of the relative minima. Provided also is a device for continuously monitoring blood glucose levels in a patient. The methods and device involve applying a Fourier approximation to blood glucose level data.