Neural Stimulation Programming With Evoked-Response Threshold Detection
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Solution Overview
Problem
Existing neuromodulation devices face challenges in maintaining optimal neural recruitment and energy efficiency due to electrode migration, postural changes, and individual patient variability, leading to ineffective or uncomfortable therapy, and prolonged programming processes.
Innovation Solution
A neural stimulation system that rapidly estimates key parameters by measuring neural response intensity using implantable electrodes and processors, determining a perceptual marker without patient reporting, and adjusting stimulus intensity through closed-loop control to maintain a therapeutic range.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If stimulus intensity is increased to maintain therapeutic effect, then neural recruitment is improved, but patient discomfort increases
Solution Approach 1:
The system uses a feedback control mechanism where the measured neural response intensity is compared to a target response intensity, and the stimulus intensity is adjusted accordingly to maintain the therapeutic effect while avoiding discomfort. The controller modifies the stimulus intensity parameter based on the error between measured and target response intensities.
Solution Approach 2:
The system dynamically changes the stimulus intensity parameter based on measured neural response characteristics. By adjusting this parameter in real-time according to the activation plot model and detected response, the system maintains efficacy while minimizing discomfort.
2Reliability
If stimulus intensity is increased to compensate for electrode migration, then neural recruitment is maintained, but energy consumption increases
Solution Approach 1:
The feedback control loop continuously monitors neural response intensity and adjusts stimulus intensity only to the extent necessary to maintain therapeutic effect. This prevents unnecessary energy consumption that would occur with fixed high-intensity stimulation to compensate for electrode migration.
Solution Approach 2:
The system dynamically adapts stimulus parameters based on real-time measurements of neural response and activation plot characteristics. This dynamic adjustment allows the system to maintain effective neural recruitment while optimizing energy consumption according to actual physiological conditions.
3Measurement precision
If traditional programming methods are used to determine patient response parameters, then accurate parameter setting is achieved, but programming time is prolonged
Solution Approach 1:
The system performs preliminary measurements of neural response characteristics across different stimulus intensities to build an activation plot model. This preliminary characterization enables rapid determination of optimal parameters without requiring time-consuming trial-and-error programming sessions.
Solution Approach 2:
The system automatically determines optimal stimulus parameters by analyzing measured neural responses and applying the activation plot model, eliminating the need for lengthy manual programming procedures. The device self-configures based on objective physiological measurements rather than subjective patient reporting.
4Reliability
If stimulus intensity is ramped high to ensure therapeutic effect, then neural recruitment is sufficient, but patient comfort is compromised
Solution Approach 1:
The system uses feedback from measured neural response intensity to determine the minimum stimulus intensity required for therapeutic effect. By comparing actual response to target response, the system adjusts intensity to the lowest effective level, ensuring comfort while maintaining efficacy.
Solution Approach 2:
The system changes stimulus intensity parameters dynamically based on the activation plot model and real-time response measurements, transitioning from fixed high-intensity stimulation to adaptive intensity modulation that maintains therapeutic effect at comfortable levels.
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
Enables efficient and comfortable neural stimulation therapy by accurately setting parameters, reducing discomfort, and minimizing energy consumption, while allowing for patient-specific adjustments.
Implementation Method 1
A stimulus source is configured to provide neural stimuli to be delivered via one or more stimulus electrodes to neural tissue of a patient in order to evoke neural responses in the neural tissue
Implementation Method 2
Measurement circuitry is configured to capture signal windows sensed at one or more sense electrodes subsequent to respective neural stimuli
Data Source
AI summary
A neural stimulation system comprising: a neuromodulation device for delivering neural stimuli, and a processor. The neurostimulation device comprises: a plurality of electrodes; a stimulus source to provide neural stimuli to be delivered via the electrodes to a neural tissue to evoke neural responses; measurement circuitry configured to capture signal windows sensed at the electrodes subsequent to respective neural stimuli; and a control unit configured to control the stimulus source to provide each neural stimulus according to a stimulus intensity parameter. The processor is configured to: instruct the control unit to control the stimulus source to sequentially provide a plurality of neural stimuli according to a ramp of stimulus intensity parameter values up to a perceptual marker; receive a captured signal window subsequent to each neural stimulus; detect whether an evoked neural response is present in each signal window; and determine the perceptual marker based on the detecting.