CGM Gain Function for ISF Lag Compensation
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Solution Overview
Problem
Continuous glucose monitoring (CGM) systems face challenges in accurately determining capillary glucose levels due to lag between interstitial fluid (ISF) and capillary glucose, as well as signal noise from sensitivity changes and tissue effects, leading to reduced accuracy in therapeutic decisions.
Innovation Solution
A method and device that employ a gain function based on sensor progression parameters to compensate for errors in glucose readings by referencing current glucose signals to previously measured signals, using a CGM device with a processor and memory to compute and communicate compensated glucose values.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If filtering or lag-compensation methods are used to reduce ISF glucose lag, then the timing accuracy of glucose readings is improved, but the reliability of glucose values deteriorates due to error propagation from ISF glucose determination
Solution Approach 1:
The patent applies feedback by continuously monitoring sensor progression parameters and using them to dynamically adjust the gain function. The system calculates ratios between current and historical glucose signals, feeds this information back through the gain function, and adjusts compensation in real-time based on observed sensor behavior patterns, thereby improving reliability while maintaining timing accuracy
Solution Approach 2:
The patent changes parameters by introducing sensor progression parameters (ratios of current to historical glucose signals) that characterize sensor behavior over time. These parameter changes enable the gain function to adapt to sensitivity drift and tissue effects, resolving the contradiction between timing accuracy and reliability by making the compensation dynamic rather than static
2Measurement precision
If sensor progression parameters and gain functions are used to compensate for sensitivity changes and tissue effects, then the accuracy of glucose readings is improved, but the device complexity increases
Solution Approach 1:
The patent applies preliminary action by pre-calculating and storing the gain function based on sensor progression parameters before actual glucose monitoring begins. This preparation work is done in advance, allowing the device to use straightforward look-up and application of pre-computed compensation factors during operation, thereby improving accuracy without adding significant real-time processing complexity
Solution Approach 2:
The gain function serves as an intermediary that mediates between raw sensor signals and final glucose readings. It absorbs the complexity of compensating for sensitivity changes and tissue effects, allowing the rest of the device to remain relatively simple while still achieving high measurement precision through this intermediate computational layer
3Measurement precision
If reference concentrations are used to determine analytical method accuracy, then the measurement validity is improved, but the ease of operation deteriorates due to difficulty in obtaining reference ISF glucose measurements
Solution Approach 1:
The patent applies self-service by using the sensor's own historical measurements to create reference data through sensor progression parameters. Instead of requiring external reference measurements, the system generates its own reference framework by analyzing ratios between current and past readings, making the system self-calibrating and eliminating the need for difficult ISF sampling
Solution Approach 2:
Sensor progression parameters act as intermediaries that bridge the gap between raw sensor signals and valid reference concentrations. These parameters translate difficult-to-obtain ISF measurements into usable reference data by comparing current readings against historical patterns, thereby maintaining measurement validity while improving ease of operation
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach significantly reduces the Mean Absolute Relative Difference (MARD) in glucose monitoring, improving accuracy and minimizing ISF lag, thereby enhancing the reliability of therapeutic actions based on glucose data.
Implementation Method 1
a sensor, a memory and a processor... measure and store a plurality of glucose signals using the sensor
Data Source
AI summary
A continuous glucose monitoring (CGM) device may include a wearable portion having a sensor configured to produce glucose signals from interstitial fluid, a processor, a memory and transmitter circuitry. The memory may include a pre-determined gain function based on a point-of-interest glucose signal and glucose signals measured prior to the point-of-interest glucose signal. The memory may also include computer program code stored therein that, when executed by the processor, causes the CGM device to (a) measure and store a plurality of glucose signals using the sensor and memory; (b) for a presently-measured glucose signal, employ the plurality of previously-measured glucose signals stored in the memory and the pre-determined gain function to compute a compensated glucose value; and (c) communicate the compensated glucose value to a user of the CGM device. Numerous other embodiments are provided.