Template-Based Artifact Reduction for ECAP Signal Extraction
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Solution Overview
Problem
Existing implantable neurostimulator devices face challenges in accurately sensing neural responses due to overlapping stimulation artifacts and neural signals, which complicates the extraction of meaningful features from evoked compound action potentials (ECAPs), leading to inaccurate closed-loop feedback adjustments.
Innovation Solution
A template-based method is employed to reduce stimulation artifacts by modeling the residual charge decay using an RC circuit model and subtracting a scaled template signal from recorded neural responses, allowing for the isolation of neural signals like ECAPs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the neurostimulator delivers electrical stimulation to evoke neural responses, then therapeutic effect is improved, but stimulation artifact interferes with neural response detection
Solution Approach 1:
The system performs preliminary actions by delivering test stimulation pulses at different amplitudes before the actual therapeutic stimulation to characterize and model the stimulation artifact. This preliminary characterization allows the system to predict and remove artifact components from subsequent neural response measurements, resolving the contradiction between delivering therapeutic stimulation and accurately detecting neural responses.
Solution Approach 2:
The system introduces an intermediary mathematical model (RC circuit model) that represents the stimulation artifact separately from the neural response. By modeling the artifact as an intermediate component that can be calculated and subtracted, the system enables clean separation of artifact and neural response signals, improving detection accuracy while maintaining therapeutic stimulation delivery.
2Object-affected harmful factors
If the residual charge decay time constant is increased to reduce artifact overlap, then artifact interference is reduced, but neural response detection timing becomes more complex
Solution Approach 1:
The system changes the parameter of residual charge decay time constant to optimize the separation between stimulation artifact and neural response. By adjusting this time constant parameter, the system reduces artifact overlap with neural responses while managing the complexity of signal processing through systematic modeling approaches.
3Measurement precision
If template-based artifact removal is applied, then neural response extraction precision is improved, but computational requirements increase
Solution Approach 1:
The system creates a simplified copy or model of the stimulation artifact using an RC circuit model, rather than requiring complex computational analysis of the actual artifact. This template copy can be scaled and subtracted from the measured signal with minimal computation, achieving high neural response extraction precision while keeping power requirements low.
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 approach effectively separates neural responses from stimulation artifacts, enabling precise feature extraction and closed-loop feedback for improved therapeutic stimulation adjustments.
Implementation Method 1
modeling the residual charge decay using an RC circuit model
Implementation Method 2
modeling the residual charge decay using an RC circuit model
Data Source
AI summary
Methods and systems for recording evoked potentials evoked during electrical stimulation of a patient's neural tissue are disclosed. The methods and systems are useful with treatment modalities, such as spinal cord stimulation (SCS). The disclosed methods and system allow for stimulation artifacts to be reduced in, or removed from, the recorded signals. A residual portion of the stimulation artifact can be modeled and subtracted from a recorded signal that includes both artifact and neural response contributions.