Glucose Signal Assessment Using Event-Triggered Segmentation
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Solution Overview
Problem
Conventional glucose monitoring methods fail to provide an intuitive analysis of blood glucose concentration excursions, as they do not account for individual patient habits such as meal timing and physical activity, making it difficult to determine the appropriate insulin bolus amount, distribution, and timing.
Innovation Solution
A method and device for processing glucose concentration signals that allow for user-triggered retrospective analysis, enabling data collection and grading based on specific events like meals or activities, using triggers such as user input, time, or physiological parameters to determine the appropriateness of insulin therapy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If conventional spot monitoring or continuous glucose monitoring methods are used, then glucose concentration data can be obtained, but the data cannot be intuitively analyzed because individual patient habits such as meal timing and physical activity are not considered
Solution Approach 1:
The patent combines glucose concentration monitoring data with patient habit data (meal timing, physical activity) into a unified analysis system. The assessment device integrates multiple data sources and synchronizes them temporally to provide comprehensive analysis that considers both physiological measurements and behavioral context, resolving the contradiction between information completeness and analytical simplicity.
Solution Approach 2:
The patent introduces an assessment device as an intermediary between raw glucose monitoring data and patient interpretation. This device performs automated analysis by comparing measured glucose values with expected values based on patient habits, generating intuitive assessments without requiring patients to manually log all activities or interpret complex raw data.
2Productivity
If fixed and predetermined measurement periods are used, then continuous monitoring can be performed, but the measurements cannot react to individual patient habits such as meal timing or physical activity
Solution Approach 1:
The patent implements dynamic assessment periods that adapt to individual patient habits rather than using fixed measurement windows. The system learns and identifies each patient's typical meal times and activity patterns, then dynamically adjusts the assessment periods to align with these individualized schedules, allowing continuous monitoring to effectively respond to varying patient behaviors.
Solution Approach 2:
The patent performs preliminary identification of patient habits through initial data collection and analysis. By pre-establishing each patient's typical meal times, activity patterns, and glucose responses, the system prepares customized assessment parameters in advance, enabling subsequent continuous monitoring to automatically adapt to individual habits without real-time manual adjustment.
3Measurement precision
If conventional monitoring methods are used without synchronization to patient habits, then glucose concentration values can be measured, but intuitive analysis is not possible without parallel logging of events
Solution Approach 1:
The patent implements a self-service system where the assessment device automatically performs habit identification and data synchronization without requiring manual patient logging. The system autonomously analyzes glucose monitoring data to identify patterns related to meals and activities, automatically aligning measurement data with corresponding events based on temporal patterns, thereby eliminating the need for complex parallel logging while preserving measurement precision.
4Ease of manufacture
If averaged data or fixed measurement programs are used, then therapy can be standardized, but the therapy does not satisfy individual patient needs and requirements
Solution Approach 1:
The patent segments the patient population into individual profiles, each with customized assessment parameters based on their unique habits and responses. Rather than applying a single fixed measurement program to all patients, the system divides the therapy approach into personalized segments, identifying and analyzing each patient's specific meal patterns, activity levels, and glucose responses to create tailored assessment protocols.
Solution Approach 2:
The patent dynamically changes assessment parameters based on individual patient characteristics and responses. The system adjusts measurement windows, target glucose ranges, and assessment criteria according to each patient's identified habits and physiological responses, transforming standardized therapy parameters into personalized settings that satisfy individual patient needs while maintaining ease of implementation through automated adaptation.
Data Source
AI summary
Embodiments of an assessment method for processing a signal corresponding to a glucose concentration and performing a retrospective analysis includes the steps of: a) initiating a first trigger, wherein the first trigger defines the beginning of a segment of a continuous measurement of glucose concentration or define the first of a series of spot measurements of glucose concentration; b) collecting data of the continuous measurement or the series of spot measurements of glucose concentration to be analyzed, wherein the collected data relates to a glucose concentration excursion corresponding to a reaction to a relevant event such as a meal or physical activity; c) analyzing the collected data initiated by a second trigger, wherein the second trigger defines the end of the collected data to be analyzed, wherein a measure for a grading of the excursion is determined from the collected data, and d) displaying the result of the analysis.