Sub-perception Neuromodulation Calibration via Space Domain Scaling
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current neuromodulation systems face challenges in accurately programming sub-perception neuromodulation fields due to patient-specific differences in electrode-tissue coupling and neural excitability, as patients do not provide real-time feedback for adjustments, making it difficult to optimize energy allocation to electrodes for effective therapy.
Innovation Solution
The system employs space domain scaling to account for displacement between electrodes and tissue, and time domain scaling to adjust for properties of the neural target or neuromodulation waveform, using feedback metrics to normalize and optimize energy allocations across electrodes, ensuring precise delivery of sub-perception modulation fields.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If sub-perception neuromodulation is delivered to provide therapeutic effect, then therapeutic effectiveness is improved, but patient feedback is unavailable for real-time adjustments
Solution Approach 1:
The system employs objective feedback metrics (such as evoked compound action potentials, local field potentials, or other neural signals) to monitor the effect of neuromodulation on neural targets. This feedback enables the system to adjust electrode parameters automatically, compensating for the absence of subjective patient feedback while maintaining therapeutic effectiveness.
Solution Approach 2:
The neuromodulation system performs self-calibration and self-adjustment by using objective feedback signals to automatically optimize electrode parameters. The system monitors its own output effects and modifies its operation accordingly, eliminating the need for continuous patient input or external intervention.
2Reliability
If energy is allocated to electrodes to provide sub-perception modulation, then therapeutic effect is achieved, but patient-specific differences in electrode-tissue coupling cause variability
Solution Approach 1:
The system dynamically adjusts electrode parameters (current amplitude, pulse width, frequency) based on objective feedback metrics and patient-specific characteristics. By changing parameters in response to measured neural responses and electrode-tissue coupling properties, the system achieves precise energy allocation tailored to each patient's unique anatomy and tissue characteristics.
Solution Approach 2:
The system applies different energy allocation strategies to different electrodes based on their specific characteristics and coupling properties. Each electrode receives customized parameter settings optimized for its local tissue environment, rather than using uniform settings across all electrodes, thereby addressing patient-specific variations in electrode-tissue coupling.
3Manufacturing precision
If space domain scaling is applied to compensate for electrode displacement, then energy allocation is optimized, but system complexity increases
Solution Approach 1:
The system performs preliminary calibration measurements during an initial procedure to characterize electrode-tissue coupling properties and determine optimal energy allocations. By completing this calibration work upfront, the system can use simplified real-time adjustments during treatment, reducing the complexity of continuous calibration operations.
Solution Approach 2:
The system introduces objective feedback metrics as intermediaries between the electrode stimulation and the desired therapeutic outcome. These feedback signals serve as mediators that enable automatic adjustment of energy allocation, simplifying the overall control architecture by providing a direct measurement link without requiring complex real-time optimization algorithms.
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 enables improved clinical outcomes by compensating for individual patient variations, enhancing the precision and effectiveness of neuromodulation therapy without perceivable side effects, such as paresthesia, by optimizing energy distribution and modulation parameters.
Implementation Method 1
delivering modulation energy to a neural target
Data Source
Figure 1~2
Figure 3~4
Figure 5
AI summary
An example of a system to program a neuromodulator to deliver neuromodulation to a neural target using a plurality of electrodes may comprise a programming control circuit configured to determine target energy allocations for the plurality of electrodes based on at least one target pole to provide a target sub-perception modulation field, calibrate a plurality of electrode groups in the plurality of electrodes where each of the plurality of electrode groups is in an electrode configuration and includes an electrode set of at least one electrode from the plurality of electrodes, including for each of the plurality of electrode groups receive a feedback metric to delivery of modulation energy to the neural target, and normalize the target sub-perception modulation field, including determine a space domain scaling factor using the feedback metric to account for actual electrode-tissue coupling, and apply the space domain scaling factor to the target energy allocations.