Analyte Sensor Signal Gap Filling and Smoothing for Reliable Monitoring

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Solution Overview

Problem

Existing analyte monitoring systems suffer from gaps and noise in time series data due to sensor errors, leading to inaccuracies and reduced trust and confidence in the system's accuracy.

Innovation Solution

A continuous analyte monitoring system that fills gaps in time series data using interpolation and smooths data points using moving average filters to enhance data completeness and reliability.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Reliability

If sensor measurements are taken continuously to provide real-time monitoring data, then the completeness of the time series data is improved, but sensor errors introduce gaps and noise that reduce data reliability

Engineering Contradiction:
Improvedata reliabilityVSAvoiddata gaps
Core Design Contradiction:
ReliabilityVSLoss of information

Solution Approach 1:

The system performs preliminary actions by detecting gaps and noise in the time series data before final analysis. It identifies missing data points and applies interpolation methods to reconstruct the complete time series, preventing data gaps from affecting subsequent monitoring and analysis operations.

Inventive Principle:
Principle #10Preliminary action

Solution Approach 2:

The system introduces an intermediary processing layer that acts between raw sensor measurements and final analysis. This layer applies filtering algorithms to remove noise and interpolation techniques to fill gaps, serving as a mediator that transforms unreliable raw data into reliable processed data for clinical decision-making.

Inventive Principle:
Principle #24Intermediary (Mediator)

2Measurement precision

If raw sensor data is used directly for analysis, then data completeness is maintained, but noise and gaps reduce the accuracy of glycemic event prediction

Engineering Contradiction:
Improvemeasurement accuracyVSAvoiddata trustworthiness
Core Design Contradiction:
Measurement precisionVSReliability

Solution Approach 1:

The system introduces an intermediary processing layer that acts between raw sensor measurements and final analysis. This layer applies filtering algorithms to remove noise and interpolation techniques to fill gaps, serving as a mediator that transforms unreliable raw data into reliable processed data for clinical decision-making.

Inventive Principle:
Principle #24Intermediary (Mediator)

Solution Approach 2:

The system implements feedback mechanisms where processed data quality is continuously evaluated. By comparing interpolated values with actual sensor readings when available, the system adjusts its processing parameters to optimize both noise reduction and gap filling, ensuring measurement precision while maintaining data trustworthiness.

Inventive Principle:
Principle #23Feedback

3Loss of information

If gap-filling interpolation is applied to complete the time series, then data completeness is improved, but computational complexity increases

Engineering Contradiction:
Improvedata completenessVSAvoidprocessing complexity
Core Design Contradiction:
Loss of informationVSDevice complexity

Solution Approach 1:

The system performs preliminary actions by detecting gaps and noise in the time series data before final analysis. It identifies missing data points and applies interpolation methods to reconstruct the complete time series, preventing data gaps from affecting subsequent monitoring and analysis operations.

Inventive Principle:
Principle #10Preliminary action

4Measurement precision

If noise filtering is applied to smooth the data, then measurement precision is improved, but data processing time increases

Engineering Contradiction:
Improvedata precisionVSAvoidprocessing time
Core Design Contradiction:
Measurement precisionVSLoss of time

Solution Approach 1:

The system introduces an intermediary processing layer that acts between raw sensor measurements and final analysis. This layer applies filtering algorithms to remove noise and interpolation techniques to fill gaps, serving as a mediator that transforms unreliable raw data into reliable processed data for clinical decision-making.

Inventive Principle:
Principle #24Intermediary (Mediator)

Data Source

PatentUS20260020781A1System and methods for completing and smoothing analyte sensor system signals
Publication Date: 2026.01.22 DEXCOM INC
  • US20260020781A1 patent drawing
  • US20260020781A1 patent drawing
  • US20260020781A1 patent drawing

AI summary

The present disclosure relates to an analyte monitoring system for completing and smoothing analyte sensor signals. The system includes an analyte sensor system, a memory, and a processor. The processor detects a first gap in a time series of analyte sensor measurements from the analyte sensor system. The first gap is between a first data point in the time series and a second data point in the time series. The processor interpolates between the first data point and the second data point to determine a fill data point and adds the fill data point to the first gap. The processor determines an average of at least (i) a value of the first data point, (ii) the fill data point, and (iii) a third data point preceding the first data point in the time series and sets the value of the first data point to the determined average.