Glucose Trace Alignment for Lifestyle Event Correlation
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Solution Overview
Problem
Existing glucose monitoring systems struggle to effectively identify correlations between glucose data and lifestyle events, making it difficult for diabetic patients to optimize their therapy and manage their condition.
Innovation Solution
A method and system for analyzing glucose monitoring data that involves plotting glucose monitoring traces on a graphical window with a time and glucose axis, segmenting the data into measurement segments, and aligning them based on trace characteristics like local maxima or minima, allowing for time-indexed free analysis to overlay and normalize the data for better correlation identification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If glucose monitoring data is collected continuously over time, then the quantity of glucose data increases, but the difficulty of identifying correlations between glucose data and lifestyle events increases
Solution Approach 1:
The patent segments glucose monitoring data into distinct traces, each representing a specific lifestyle event (e.g., meals, exercise). By dividing the continuous glucose data into event-specific segments, the system enables targeted analysis of correlations between glucose levels and specific lifestyle events, making the analysis more manageable despite the large volume of data.
Solution Approach 2:
The patent introduces a new dimension for data organization by aligning glucose traces based on their shape characteristics (local maxima, minima, inflection points) rather than just time. This dimensional transformation allows multiple glucose traces to be overlaid and compared in a normalized format, revealing patterns and correlations that would be difficult to detect in traditional time-series representations.
2Reliability
If multiple glucose monitoring traces are analyzed simultaneously, then the ability to identify patterns improves, but the complexity of data processing increases
Solution Approach 1:
The patent performs preliminary processing of glucose traces by identifying and marking key characteristics (local maxima, minima, inflection points) before alignment. This pre-processing step simplifies the subsequent alignment and overlay operations, reducing the computational complexity of analyzing multiple traces simultaneously while improving pattern identification reliability.
Solution Approach 2:
The patent transforms glucose traces by changing their temporal parameters through normalization based on characteristic points. By adjusting the time alignment of traces according to their shape characteristics rather than absolute time, the system simplifies the comparison process and enables more reliable pattern identification across multiple traces.
3Loss of time
If glucose data is aligned based on time, then chronological accuracy is maintained, but the ability to compare similar glucose patterns across different events is reduced
Solution Approach 1:
The patent adds a shape-based alignment dimension to the traditional time-based representation. By aligning glucose traces according to their characteristic points (local maxima, minima, inflection points), the system creates a normalized view that preserves chronological information while enabling direct comparison of glucose patterns across different events, thereby improving pattern comparison reliability without completely sacrificing temporal accuracy.
Data Source
AI summary
A computer-implemented method and a system for analyzing glucose monitoring data indicative of a glucose level, comprising: presenting by a data processing device provided a graphical window on a display device wherein the graphical window comprises a time abscissa axis that defines time units, and a glucose ordinate axis that defines glucose units; receiving a plurality of glucose monitoring values associated with a monitoring time period by the data processing device; segmenting the plurality of glucose monitoring values into a plurality of glucose monitoring traces indicative of the glucose level; defining a reference trace segment; determining the reference trace segment for each of the plurality of glucose monitoring traces; and plotting, for a time window comprising the reference trace segment, the plurality of glucose monitoring traces within the graphical window. Furthermore, a non-transitory computer readable medium is disclosed.