Compression event detection for continuous glucose monitors
By employing multiple analyte sensors to detect and compensate for compression events in continuous glucose monitoring systems, the system addresses inaccuracies in glucose measurements, enhancing the accuracy and reliability of glucose monitoring.
WO2025117512A1PCT designated stage expired Publication Date: 2025-06-05DEXCOM INC
Patent Information
- Application Number
- PCT/US2024/057408
- Authority / Receiving Office
- WO · WO
- Patent Type
- Applications
- Current Assignee / Owner
- Priority Date
- 2023-11-27
- Filing Date
- 2024-11-26
- Publication Date
- 2025-06-05
AI Technical Summary
Technical Problem
Existing continuous glucose monitoring systems face inaccuracies in glucose measurements due to sensor compression, which can lead to false indications of sensor failure.
Method used
The system detects and compensates for compression events by using multiple analyte sensors to evaluate lactate and glucose levels, comparing expected values calculated using a filter to actual values, and adjusting or blanking glucose samples accordingly.
Benefits of technology
This approach effectively differentiates between compensatable and acute compression events, reducing false alarms and ensuring accurate glucose monitoring.
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Smart Images
Figure US2024057408_05062025_PF_FP_ABST
Abstract
A continuous analyte monitoring system includes first and second analyte sensors configured to sense analytes such as lactate and glucose in the tissue of a user. A controller Is coupled to the analyte sensors and configured evaluate first samples of outputs of the first analyte sensor and second samples of outputs of the second analyte sensor with respect to one another to determine whether the first samples and the second samples indicate compression of the tissue. If the first samples and the second samples indicate compression of the tissue, compensate for the compression of the tissue with respect to the first samples. The controller may evaluate the machine learning models using a machine learning model or a filter.
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Citation Information
Patent Citations
Fault discrimination and responsive processing based on data and context
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Systems and methods for multi-analyte sensing
US20230263439A1