Carbon Nanotube Temporal Impedance Biosensing for BNP Detection
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Solution Overview
Problem
Current BNP testing for heart failure diagnosis is laborious, requiring bulky equipment and trained professionals, and thin film sensors face variability and noise issues, making point-of-care use challenging.
Innovation Solution
A system combining carbon nanotube thin film sensors with machine learning models to process temporal impedance spectra for accurate BNP detection, using a sample cartridge, sensor reader, and data processing apparatus for reliable point-of-care analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional immunoassay equipment is used for BNP testing, then measurement precision is improved, but device complexity and ease of operation worsen
Solution Approach 1:
The patent replaces conventional mechanical immunoassay equipment with an electrochemical sensing system that uses electrical impedance measurements to detect BNP. The electrochemical sensor with carbon nanotube thin film and antibody coating detects BNP through electrical signal changes rather than mechanical/optical immunoassay procedures, thereby simplifying the device while maintaining clinical-grade detection accuracy
Solution Approach 2:
The patent introduces an intermediary machine learning processing layer that mediates between the raw electrochemical sensor signals and the final BNP concentration determination. This intermediary component processes temporal impedance spectra to extract accurate BNP levels, enabling the use of simpler sensors while maintaining measurement precision through computational enhancement
2Measurement precision
If conventional immunoassay equipment is used for BNP testing, then measurement precision is improved, but ease of operation worsens
Solution Approach 1:
The patent replaces complex mechanical immunoassay procedures with automated electrochemical measurement and machine learning analysis. The system automatically performs impedance spectroscopy sweeps, processes temporal spectra through trained machine learning models, and outputs BNP concentrations, eliminating the need for trained professionals to perform manual immunoassay procedures
Solution Approach 2:
The patent implements a self-service system where the machine learning model automatically processes sensor data and determines BNP concentrations without requiring trained professionals. The system performs self-calibration and self-diagnosis through the machine learning processing layer, enabling point-of-care use by non-specialized personnel
3Productivity
If thin film sensors are used for BNP detection, then productivity is improved, but measurement precision worsens due to sensor variability and noise
Solution Approach 1:
The patent applies preliminary action by training machine learning models in advance using labeled datasets that capture sensor variability and noise characteristics. The pre-trained models are then deployed to process new sensor data, enabling the system to compensate for thin film sensor variability and noise without requiring additional calibration or adjustment during actual BNP detection
Solution Approach 2:
The patent implements feedback through the machine learning processing pipeline that continuously analyzes temporal impedance spectra and adjusts BNP concentration determinations based on learned patterns of sensor variability and noise. The system uses the temporal evolution of impedance spectra as feedback to distinguish true BNP signals from sensor noise, thereby maintaining measurement precision while utilizing fast thin film sensors
4Measurement precision
If BNP testing is performed using current methods, then measurement precision is improved, but loss of time increases
Solution Approach 1:
The patent replaces time-consuming mechanical immunoassay procedures with rapid electrochemical impedance measurements. The electrochemical sensor system performs multiple impedance spectroscopy sweeps in minutes rather than hours, and the machine learning processing rapidly analyzes the temporal spectra to determine BNP concentrations, thereby reducing testing time while maintaining clinical-grade accuracy
Solution Approach 2:
The patent implements continuous measurement by performing multiple impedance spectroscopy sweeps over time to generate temporal impedance spectra. This continuous data collection approach allows the machine learning model to extract accurate BNP concentrations from the temporal evolution of sensor signals, achieving both speed and accuracy by utilizing continuous rather than single-point measurements
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
Enables rapid, accurate, and reproducible BNP concentration determination in blood samples, reducing the need for complex equipment and specialized personnel, suitable for point-of-care settings.
Implementation Method 1
A dynamic impedance spectrum of the sensor is dependent on the concentration of analyte in the fluid sample when contacted by the fluid sample
Data Source
AI summary
A system for determining a concentration of an analyte, such as B-type natriuretic peptide (BNP), in a fluid sample, such as a blood sample, includes a sample cartridge, a sensor reader, and a data processing system. The cartridge comprises a sample chamber with an electrochemical sensor, such as a carbon nanotube thin film sensor, and a connector for coupling with the sensor reader. The sensor reader is operable to perform impedance spectroscopy sweeps on the sensor at preconfigured time intervals to generate a set of temporal impedance spectra, which is communicated to the data processing apparatus. The latter operates a trained machine learning model on the set of temporal impedance spectra to generate the concentration of the analyte in the fluid sample. The system enables convenient and reliable point-of-care analyte concentration determination for prognostication of a patient condition.