Neural Stimulation Programming for Accurate ECAP Measurement
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Solution Overview
Problem
Neuromodulation systems face challenges in maintaining appropriate neural recruitment and energy efficiency due to electrode migration, postural changes, and the difficulty in accurately measuring neural responses evoked by stimuli, which are often masked by stimulus crosstalk and artefacts, and existing technologies require costly and time-consuming programming processes.
Innovation Solution
A system and method for programming closed-loop neural stimulation devices that iteratively refine the selection of measurement electrode configurations and ECAP detector parameters using prior patient data and feedback mechanisms to optimize neural response measurement, reducing the need for manual re-programming.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional manual programming methods are used to select measurement electrode configurations and ECAP detector parameters, then the programming process can be completed, but it requires significant time and cost resources
Solution Approach 1:
The system performs automated selection of measurement electrode configurations and ECAP detector parameters using probabilistic algorithms and iterative refinement processes. The device independently evaluates multiple configurations, measures neural response quality, and converges on optimal settings without requiring extensive manual intervention from clinicians, thereby dramatically reducing programming time and cost while maintaining or improving programming quality
Solution Approach 2:
The system implements an iterative feedback mechanism where initial probabilistic selections of measurement configurations are tested by actually measuring neural responses, the quality of these measurements is evaluated, and the probability distributions are refined based on the feedback. This closed-loop process continues until convergence, ensuring optimal settings are found efficiently through data-driven refinement rather than exhaustive manual testing
2Reliability
If stimulus intensity is increased to ensure sufficient neural recruitment, then therapeutic effect is improved, but uncomfortable or painful percepts arise due to over-recruitment of Aβ fibres
Solution Approach 1:
The system replaces subjective patient feedback about comfort and therapeutic effect with objective neural response measurements (ECAPs). By measuring the actual neural recruitment through evoked compound action potentials and using probabilistic modeling to interpret these signals, the system objectively determines the optimal stimulus intensity that achieves sufficient neural recruitment without causing discomfort, eliminating the need to rely on patient subjective reports
Solution Approach 2:
The system dynamically adjusts stimulus intensity based on measured neural response characteristics and probabilistic assessments of recruitment status. Rather than using fixed intensity levels, the system continuously monitors ECAP amplitudes and other neural response parameters, then modifies stimulus intensity to maintain optimal therapeutic effect while staying below discomfort thresholds, adapting to changes in electrode position and tissue properties over time
3Measurement precision
If ECAP measurement is performed to accurately assess neural recruitment, then stimulus intensity can be optimized, but the neural response is masked by stimulus crosstalk and artefacts
Solution Approach 1:
The system extracts the neural response signal from the composite measurement by identifying and separating the ECAP components from the stimulus artefacts and crosstalk. Using probabilistic signal processing and template matching techniques, the system isolates the relevant neural response features (such as the P1-N1-P2 waveform characteristics) from the contaminating stimulus-related signals, enabling accurate neural recruitment assessment even in the presence of significant artefacts
Solution Approach 2:
The system introduces probabilistic modeling and statistical signal processing as intermediary layers between the raw measurement signal and the final neural recruitment assessment. Rather than directly interpreting the contaminated ECAP signal, the system uses probability distributions to model the expected signal characteristics under different conditions, allowing the true neural response to be inferred despite the presence of masking artefacts and crosstalk
Data Source
AI summary
Disclosed is a method of computing a probability distribution of the suitability of measurement electrode configurations for a neuromodulation device. The method comprises: computing the probability distribution of suitability of measurement electrode configurations from: a predetermined stimulus program vector of the neuromodulation device; and prior patient data. Also disclosed is an automated method of setting a measurement electrode configuration for a neuromodulation device configured to deliver a neural stimulus to a neural pathway of a patient. The method comprises: obtaining an initial measurement electrode configuration for a predetermined stimulus program vector from a prior probability distribution of suitability of measurement electrode configurations; delivering a plurality of neural stimuli of different stimulus intensities to the neural pathway according to the stimulus program vector; measuring intensities of neural responses evoked by the neural stimuli using a current measurement electrode configuration; computing one or more quality measures of the evoked neural responses using the measured neural response intensities and the respective stimulus intensities; refining the probability distribution of suitability of measurement electrode configurations using the one or more quality measures; and obtaining a new measurement electrode configuration for the predetermined stimulus program vector using the refined probability distribution of suitability of measurement electrode configurations.