Automated Stimulus Artifact Removal in Nerve Conduction Studies
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Solution Overview
Problem
Current automated nerve conduction studies (NCS) face challenges in accurately removing stimulus artifacts, which can distort sensory signals and lead to incorrect diagnoses, especially when artifacts overlap with the signal of interest and in noisy data conditions, requiring advanced methods that do not necessitate specialized user training or additional hardware.
Innovation Solution
A novel method and apparatus for automated stimulus artifact removal that develops a physically derived model of the stimulus artifact based on known properties of the acquisition hardware and stimulator, allowing for robust removal of artifacts even when they overlap with the signal of interest, using procedures such as modeling, parameter estimation, and goodness-of-fit determination, without the need for a reference channel or extensive waveform averaging.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If traditional automated NCS devices are used, then automation and ease of operation are improved, but stimulus artifacts distort sensory signals and reduce measurement precision
Solution Approach 1:
The patent introduces a reference channel as an intermediary that captures only the stimulus artifact without the neural signal. This reference channel acts as a mediator that allows the system to separately identify and remove the artifact component from the main recording channel, thereby preserving measurement precision while maintaining automation.
Solution Approach 2:
The patent segments the recorded signal into two distinct components: the stimulus artifact (captured in the reference channel) and the neural signal (present in both channels). By separating these components through mathematical operations, the system can process each independently, improving measurement precision without sacrificing automation.
2Measurement precision
If manual artifact removal methods are used, then measurement precision may be improved, but device complexity and difficulty of operation increase due to requiring specialized training
Solution Approach 1:
The system performs artifact removal automatically using embedded algorithms that compute the artifact contribution in the main channel based on the reference channel data. This self-service capability eliminates the need for user intervention or specialized training, maintaining measurement precision while reducing device complexity from the user perspective.
Solution Approach 2:
The system uses feedback from the reference channel to continuously adjust and refine the artifact estimation in the main channel. This closed-loop approach ensures high measurement precision while keeping the process automated and simple to operate.
3Ease of operation
If hardware blanking is used to remove artifacts, then ease of operation is improved, but measurement precision deteriorates when artifacts overlap with neural signals
Solution Approach 1:
Instead of简单地 blanking the entire recording channel during artifact periods, the patent uses the reference channel as an intermediary to selectively estimate only the artifact portion. This allows precise removal of overlapping artifacts while preserving the neural signal, maintaining measurement precision without complicating operation.
Solution Approach 2:
The system dynamically changes the processing parameters based on the presence and magnitude of artifacts detected in the reference channel. When artifacts are present, the system applies artifact removal algorithms; when absent, it processes the signal normally. This adaptive approach maintains precision across varying conditions while keeping operation simple.
4Measurement precision
If waveform averaging is used to reduce noise, then measurement precision improves, but loss of time increases due to requiring extensive averaging
Solution Approach 1:
The patent replaces the mechanical approach of repeated stimulation and waveform averaging with a mathematical signal processing approach. By using the reference channel to model and subtract the artifact, the system achieves noise reduction and precision improvement through computational methods rather than temporal averaging, significantly reducing testing time.
Solution Approach 2:
The reference channel serves as an intermediary that enables artifact removal without requiring multiple trials. This single-trial capability eliminates the time loss associated with extensive waveform averaging while maintaining measurement precision through mathematical subtraction of the modeled artifact.
Data Source
AI summary
A method for the automated removal of a stimulus artifact from an electrophysiological signal waveform, wherein the novel method comprises:providing a model of the stimulus artifact that is physically derived and is based on known properties of the electrophysiological signal waveform acquisition hardware and stimulator; andfiltering the stimulus artifact out of the electrophysiological signal waveform using the model.