Multi-Analyte Sensor Recalibration for Cross-Talk and Drift
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Solution Overview
Problem
Existing multi-analyte monitoring systems face challenges such as cross-talk between sensors, oxygen limitation, selectivity and diffusivity issues, and inaccurate characterizations due to local analyte interactions, leading to inaccurate glycemic event predictions and management of diabetes.
Innovation Solution
A continuous multi-analyte monitoring system using voxelated working electrodes with specific enzymes and aptamers, coupled with a potentiostat and impedance analyzer, enables simultaneous multiplexed sensing of multiple analytes like glucose and lactate, along with automated calibration and compensation for artifacts and sensor wear location.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If multiple sensors are used for multi-analyte sensing, then the ability to monitor multiple analytes simultaneously is improved, but cross-talk between sensors increases leading to reduced sensing accuracy
Solution Approach 1:
The sensor array is segmented into spatially separated sensing zones, each dedicated to detecting a specific analyte. This physical segmentation prevents cross-talk between sensors while maintaining multi-analyte detection capability through localized measurement regions.
Solution Approach 2:
Each sensing zone is equipped with analyte-specific recognition elements (such as enzymes or antibodies) that provide local selectivity. This ensures that each sensor responds primarily to its target analyte while being insensitive to others, eliminating cross-interference.
2Adaptability or versatility
If multiple sensors are used for multi-analyte sensing, then comprehensive physiological monitoring is improved, but hardware and software complexity increases
Solution Approach 1:
Multiple sensing zones are integrated into a single sensor platform sharing common infrastructure including substrate, fluidic channels, and electronic readout circuitry. This merging approach enables multi-analyte monitoring while reducing overall system complexity compared to separate sensor systems.
Solution Approach 2:
The sensor platform is designed with universal components that serve multiple functions: a common substrate supports multiple sensing zones, shared electronic circuits read out signals from all sensors, and integrated data processing handles multiple analyte streams, reducing hardware and software complexity.
3Device complexity
If a common resistance layer is used for multiple analytes, then sensor structure is simplified, but maintaining selectivity and diffusivity for each analyte becomes challenging
Solution Approach 1:
The resistance layer is segmented into spatially distinct regions corresponding to each sensing zone, with each region optimized for its target analyte's transport properties while sharing the common layer structure.
Solution Approach 2:
Different portions of the common resistance layer are engineered with locally optimized properties: pore size, charge density, or hydrophobicity is adjusted in each zone to match the specific diffusivity and selectivity requirements of the target analyte for that sensing zone.
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
Provides more accurate and reliable determinations of physiological states, enabling better decision-making for diabetes management by improving glycemic event predictions and sensor accuracy through machine learning models.
Implementation Method 1
Each of the one or more voxelated working electrodes comprises a one or more enzymes deposited thereon
Implementation Method 2
A continuous multi-analyte monitoring system using voxelated working electrodes with specific enzymes and aptamers
Implementation Method 3
coupled with a potentiostat and impedance analyzer, enables simultaneous multiplexed sensing of multiple analytes
Implementation Method 4
coupled with a potentiostat and impedance analyzer, enables simultaneous multiplexed sensing of multiple analytes
Data Source
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AI summary
An apparatus includes an analyte sensor, a memory, and a processor. The processor monitors, using the analyte sensor, an analyte of a patient during a time period to obtain measured analyte data for the analyte and monitors other measured sensor data indicative of a physiological state of the patient during the time period. The processor also determines, based on the physiological state of the patient during the time period, expected analyte data for the analyte and determines a correction factor based on the expected analyte data and the measured analyte data. The correction factor is indicative of an error in calibration of the analyte sensor. The processor also determines whether recalibration of the analyte sensor is possible. If recalibration is possible, the processor recalibrates the analyte sensor based on the correction factor, and if recalibration is not possible, the processor recommends, to the patient, to replace the analyte sensor.