Neurotransmitter Concentration Measuring Apparatus Using Deep Learning
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Solution Overview
Problem
Current technologies, such as microdialysis and fast-scan cyclic voltammetry (FSCV), face limitations in simultaneously measuring tonic and phasic levels of multiple neurotransmitters over time with high temporal resolution due to background charging current interference, which hinders the analysis of dynamic dopamine and other neurotransmitter concentrations in the brain.
Innovation Solution
A deep learning-based approach that processes FSCV data using second-derivative-based background removal (SDBR) to isolate faradaic current from capacitive charging current, enabling the construction of a deep learning model to estimate concentrations of multiple neurotransmitters like dopamine, epinephrine, norepinephrine, and serotonin in real-time, allowing for long-time measurement with high temporal resolution.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If fast-scan cyclic voltammetry (FSCV) is used to measure neurotransmitter concentrations with high temporal resolution, then the ability to capture rapid neurotransmitter dynamics is improved, but background charging current causes continuous current rise that makes long-period measurement difficult
Solution Approach 1:
The patent segments the total current signal into two distinct components: capacitive charging current and faradaic current. By applying second-derivative-based background removal (SDBR) processing, the system separates these components mathematically, allowing the faradaic current (which contains neurotransmitter concentration information) to be extracted and measured independently over extended periods without the confounding continuous rise of background charging current.
Solution Approach 2:
The patent extracts the faradaic current component from the total FSCV current signal by removing the capacitive charging current through SDBR processing. This extraction isolates the neurochemically relevant signal (faradaic current) from the interfering background signal (capacitive charging current), enabling long-duration measurements while preserving high temporal resolution.
2Measurement precision
If second-derivative-based background removal (SDBR) is used to extract tonic concentration information, then the ability to measure tonic levels is improved, but the capability to simultaneously measure multiple neurotransmitters is limited
Solution Approach 1:
The patent introduces a deep learning model as an intermediary processing layer between the SDBR-processed voltammograms and the final neurotransmitter concentration estimates. This deep learning model is trained to recognize and differentiate the unique electrochemical signatures of multiple neurotransmitters (dopamine, serotonin, norepinephrine, epinephrine) simultaneously, enabling the system to overcome the limitation of SDBR and achieve multi-neurotransmitter measurement capability while maintaining tonic level detection accuracy.
3Measurement precision
If standard FSCV technology is used, then high sensitivity for phasic neurotransmitter monitoring is achieved, but analysis of long-period data becomes difficult due to background drift
Solution Approach 1:
The patent applies SDBR processing as a preliminary action to remove background charging current effects before the actual neurotransmitter concentration analysis is performed. By preprocessing the FSCV data to eliminate the continuous background drift, the system simplifies subsequent analysis and enables both phasic and tonic neurotransmitter level measurements without the confounding effects of background current rise.
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 method enhances the ability to simultaneously measure various neurotransmitters over a long time with high temporal resolution, improving the analysis of brain function and disease understanding by extracting faradaic current independently of capacitive charging current, thereby improving the detailed analysis rate of neurotransmitter signals.
Implementation Method 1
A deep learning-based approach that processes FSCV data using second-derivative-based background removal (SDBR) to isolate faradaic current from capacitive charging current
Data Source
AI summary
A neurotransmitter concentration measuring apparatus includes a data collecting unit configured to collect fast-scan cyclic voltammetry (FSCV) data where capacitive charging current is included in faradaic current varying depending on injection concentration for each of multiple neurotransmitters, a data processing unit configured to process the FSCV data as second-derivative-based background removal (SDBR) data in a faradaic current form where the charging current is excluded, based on a second derivative for voltage of an individual voltammogram generated for each scan by background subtraction in the FSCV data, a deep learning processing unit configured to build a deep learning model that simultaneously estimates the concentration of the multiple neurotransmitters by learning the SDBR data with a deep learning network and a measurement result providing unit configured to simultaneously provide concentration measurement results of a neurotransmitter varying depending on real-time injection for each of the multiple neurotransmitters based on the learning model.