Sensor Array Signal Detection Using Mixed Statistical-Numerical Models
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Solution Overview
Problem
Existing biosensor array detection systems face challenges in optimizing the trade-off between sensitivity and specificity, particularly in robust environments, due to their dependence on operational conditions and the need for standard datasets for calibration.
Innovation Solution
A method and system that combines statistical and numerical analysis for biosensor data, using Principal Component Analysis (PCA) for signal detection and customized numerical models for pattern recognition, allowing for real-time self-calibration and robust operation without frequent model recalibration.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If analytical models are used for quantitative data analysis, then measurement precision is improved, but adaptability deteriorates because they are only applicable for quantitative data analysis and require standard datasets for calibration
Solution Approach 1:
The patent combines statistical models and analytical models into a composite detection system. The statistical model component handles robustness and adaptability to varying operational conditions, while the analytical model component provides precise quantitative analysis. This composite approach allows the system to maintain high detection precision across different environments without requiring frequent recalibration with standard datasets.
2Adaptability or versatility
If statistical models are used to capture all correlations in datasets, then adaptability is improved, but measurement precision deteriorates because some correlations may be generated by causes that are not of interest
Solution Approach 1:
The patent segments the data analysis process into two distinct stages: first, a statistical model captures broad correlations and patterns in the data to provide adaptability; second, an analytical model performs precise quantitative analysis on the statistically processed data to ensure measurement precision. This segmentation allows each model to perform its strength while mitigating its weaknesses.
Solution Approach 2:
The statistical model acts as an intermediary between the raw sensor data and the analytical model. It pre-processes the data by capturing correlations and reducing dimensionality, thereby preparing the data for precise quantitative analysis by the analytical model while filtering out some of the noise from uninteresting correlations.
3Measurement precision
If standard datasets are collected to calibrate models, then measurement precision is improved, but loss of time increases due to the need for calibration under different operational conditions
Solution Approach 1:
The statistical model component operates autonomously to adapt to different operational conditions without requiring manual calibration with standard datasets. It self-adjusts by capturing correlations in the incoming data streams, thereby eliminating the time-consuming calibration process while maintaining detection precision through the subsequent analytical model analysis.
Solution Approach 2:
The statistical model performs preliminary data processing and pattern recognition before the analytical model conducts precise quantitative analysis. This preliminary action prepares the data in advance, reducing the need for time-consuming calibration steps and enabling faster deployment across different operational conditions.
Data Source
AI summary
A method and system for detecting and classifying biosensor and chemical sensor data includes processing data fetched from an array of sensors. A statistical analysis of the processed data is performed, to obtain statistically analyzed sensor data. A determination is made, based on the statistically analyzed sensor data, whether or not there a response signal has been generated from the array of sensors. If a response signal is determined to have been generated, a numerical pattern recognition is performed on the data collected from the array of sensors, in order to categorize the data.