Glucose Trend Analysis for Dynamic Meal Start and Peak Detection
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Solution Overview
Problem
Existing methods for determining pre-prandial and post-prandial meal responses in glucose monitoring are prone to estimation errors due to variability in meal timing, reliance on user-entered markers, and inconsistency in meal timing routines, leading to unreliable insulin therapy adjustments.
Innovation Solution
A method and apparatus for estimating meal start and peak events by analyzing real-time or pseudo-retrospective data from analyte monitoring systems, involving data conditioning, time derivative calculations, and refinement of candidate pairs to accurately identify meal start and peak responses, using forward and backward time windows and least-squares error fitting.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If paired fingerstick blood glucose tests are used to determine meal responses, then measurement data is obtained, but estimation errors occur due to variability in meal timing duration
Solution Approach 1:
The system dynamically identifies meal events by detecting changes in the rate of change of glucose levels rather than relying on fixed time intervals. The algorithm adapts to variable meal timing by using real-time derivative calculations to identify when glucose levels begin and peak, making the measurement system flexible to individual patient patterns without requiring predetermined time windows.
Solution Approach 2:
The system uses continuous glucose monitoring data with feedback loops to refine meal event identification. By continuously analyzing glucose level changes and comparing against identified meal events, the system can adjust and improve its detection accuracy over time, reducing estimation errors while maintaining reliability across variable meal timing scenarios.
2Loss of information
If user-entered meal markers are used to identify meal events, then meal timing information is obtained, but accuracy is affected by patient schedule and unforeseeable circumstances
Solution Approach 1:
The system performs self-service by automatically detecting meal events through analysis of glucose level patterns without requiring user input. The algorithm independently identifies meal start and peak times by detecting characteristic changes in glucose dynamics, eliminating reliance on user-entered markers that may be inaccurate or incomplete due to patient schedule variations or forgetfulness.
Solution Approach 2:
The system replaces the mechanical approach of manual user input with an automated computational system that detects meal events through mathematical analysis of glucose data. This substitution of manual marking with algorithmic detection eliminates the reliability issues associated with user-entered markers while preserving complete meal timing information.
3Ease of operation
If predetermined time of day windows are used to estimate meal responses, then meal timing is standardized, but reliability depends on consistency in patient meal timing routine
Solution Approach 1:
The system replaces static predetermined time windows with dynamic event-based detection that adapts to each patient's actual meal timing patterns. By identifying meals based on real-time glucose level changes rather than fixed clock times, the system maintains ease of operation through automated detection while improving reliability by accurately capturing variable meal timing without requiring patient consistency.
Solution Approach 2:
The system performs preliminary analysis of glucose data patterns to establish patient-specific meal timing characteristics before making measurements. By pre-identifying individual eating patterns and meal event structures, the system can then reliably detect meal responses without depending on standardized time windows or consistent patient routines, accommodating natural variability in meal timing.
4Measurement precision
If dense glucose measurements are collected with user-entered meal markers, then pre-prandial and post-prandial responses can be determined, but false detections occur due to signal artifacts
Solution Approach 1:
The system converts potentially harmful signal artifacts into beneficial detection opportunities by using the rate of change of glucose levels as the primary detection criterion. Rapid changes in glucose derivatives are characteristic of true meal events, while artifacts typically produce different temporal patterns. This approach transforms the presence of noise into a means of distinguishing true events from false detections through pattern recognition in the derivative data.
Solution Approach 2:
The system introduces mathematical derivatives as an intermediary layer between raw glucose measurements and meal event detection. By analyzing the rate of change and acceleration of glucose levels rather than direct glucose values, the system creates a intermediate representation that enhances the signal-to-noise ratio, making true meal events more distinguishable from signal artifacts while preserving measurement precision.
Data Source
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AI summary
Systems, methods and apparatus are provided for estimating meal start and peak meal response times are provided based on time series of sampled glucose data collected. Numerous additional aspects are disclosed.