Glucose Alarm Threshold Adaptation via CGM-User Data Convergence
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Solution Overview
Problem
Existing glucose monitoring devices face challenges in providing accurate and timely alarms for hypoglycemic events due to signal artifacts and divergence between continuous glucose monitoring (CGM) measurements and user-provided data, leading to false alarms and delayed detection of true hypoglycemic conditions.
Innovation Solution
A system that uses a data processing component to determine a best-estimate of glucose concentration by combining CGM data with user input, modifying alarm conditions based on convergence between these sources, and implementing conditional time delays to differentiate between signal artifacts and true hypoglycemic events, thereby reducing false alarms and ensuring timely detection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If alarm thresholds are set to be highly sensitive to detect true hypoglycemic events, then detection accuracy is improved, but false alarms increase due to signal artifacts
Solution Approach 1:
The patent implements dynamic alarm threshold adjustment by continuously adapting thresholds based on the user's individual glucose patterns, historical data, and contextual information. The thresholds are not fixed but evolve over time to distinguish between true hypoglycemic events and signal artifacts, resolving the contradiction between high sensitivity and false alarm reduction
Solution Approach 2:
The system incorporates feedback mechanisms where alarm history, user responses to alarms, and glucose trend data are continuously fed back into the alarm algorithm. This feedback loop enables the system to learn from false alarms and adjust future alarm behavior, improving detection accuracy while reducing false positives through iterative optimization
2Reliability
If alarm thresholds are set to reduce false alarms, then reliability is improved, but detection sensitivity decreases and true hypoglycemic events may be missed
Solution Approach 1:
The system dynamically adjusts alarm thresholds based on real-time glucose trends, rate of change, and contextual factors. When the system detects patterns consistent with true hypoglycemia (rapid decline, physiological plausibility), it lowers thresholds to maintain sensitivity, while raising thresholds during periods prone to artifacts, thus balancing reliability and detection accuracy
Solution Approach 2:
The patent changes multiple parameters simultaneously including threshold values, time delays, and weighting factors in the alarm algorithm. By adjusting these parameters dynamically based on glucose patterns and historical performance, the system optimizes the balance between reducing false alarms and maintaining detection sensitivity for true events
3Duration of action of stationary object
If continuous monitoring is performed without user input validation, then monitoring continuity is improved, but false alarms increase due to data divergence
Solution Approach 1:
The system performs preliminary validation of CGM data against user-provided glucose measurements before using them for alarm decisions. By pre-screening data quality and identifying divergence patterns early, the system can weight or exclude unreliable CGM readings, maintaining continuous monitoring while preventing false alarms from divergent data
Solution Approach 2:
The patent introduces user-provided glucose measurements as an intermediary validation layer between CGM data and alarm generation. When user inputs agree with CGM readings, the system confidence increases; when they diverge, the system uses the user input as a reference point or reduces reliance on CGM data for alarm decisions, thus maintaining continuity while improving reliability
Data Source
AI summary
Methods of determining when to activate an analyte, e.g. glucose, related alarm, such as a hypoglycemia alarm, of a continuous analyte monitor is provided. Also provided are systems, devices and kits.