Closed-Loop Neural Stimulation for Stable Therapeutic Recruitment
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Solution Overview
Problem
Existing neuromodulation systems face challenges in maintaining optimal neural recruitment and energy efficiency due to electrode migration, postural changes, and the need for personalized parameter settings, which can lead to ineffective or uncomfortable therapy.
Innovation Solution
A closed-loop neural stimulation system that uses feedback control to adjust stimulus intensity based on patient-specific data and qualitative feedback to determine optimal controller gains, ensuring therapeutic efficacy and comfort.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If stimulus intensity is increased to maintain neural recruitment above recruitment threshold, then therapeutic effect is improved, but discomfort or pain may arise due to over-recruitment of Aβ fibres
Solution Approach 1:
The system employs a feedback controller that continuously monitors the neural response and adjusts stimulus intensity to maintain recruitment within the therapeutic range. The controller uses the measured neural response as feedback to dynamically modulate stimulus parameters, preventing both under-recruitment (ineffective therapy) and over-recruitment (discomfort/pain).
Solution Approach 2:
The stimulus intensity is made dynamic rather than fixed, allowing real-time adjustment based on changing physiological conditions. The feedback controller continuously adapts the stimulus parameters to maintain optimal neural recruitment despite variations in electrode position, patient posture, or tissue properties.
2Adaptability or versatility
If electrode array migrates or patient posture changes, then neural recruitment efficacy is altered, but maintaining therapeutic range becomes more difficult
Solution Approach 1:
The feedback controller compensates for electrode migration and posture changes by continuously monitoring neural response and adjusting stimulus intensity accordingly. This closed-loop control maintains therapeutic efficacy despite physical displacements without requiring complex mechanical constraints or multiple electrodes.
Solution Approach 2:
The system self-adjusts to maintain optimal neural recruitment by using the neural response itself as the control signal. The feedback loop automatically compensates for positional changes without external intervention, making the system adaptable to migration and posture variations.
3Reliability
If stimulus intensity is maintained at high levels to ensure therapeutic effect, then neural recruitment is sufficient, but energy consumption increases
Solution Approach 1:
The stimulus intensity is dynamically adjusted to the minimum level required to achieve therapeutic neural recruitment. Rather than maintaining a fixed high intensity, the feedback controller modulates stimulus parameters in real-time, reducing energy consumption when full intensity is not needed while ensuring adequate recruitment when required.
Solution Approach 2:
The system changes stimulus parameters (intensity, pulse width, frequency) based on feedback to optimize the balance between neural recruitment and energy consumption. By adjusting multiple parameters rather than simply increasing intensity, the system achieves therapeutic effect with lower overall energy expenditure.
4Reliability
If personalized therapy settings are implemented to account for individual patient characteristics, then therapeutic efficacy is improved, but programming complexity and time increase
Solution Approach 1:
The system performs self-programming by automatically determining optimal stimulus parameters based on individual patient neural response characteristics. The feedback controller adapts to each patient's unique physiology without requiring extensive manual programming, reducing clinician time while maintaining personalized therapeutic efficacy.
Solution Approach 2:
The system uses feedback from individual patient neural responses to automatically personalize therapy settings. By measuring the patient's specific neural recruitment characteristics and using this feedback to tune parameters, the system achieves personalized treatment without manual programming of each parameter.
Data Source
AI summary
A neuromodulation device comprises stimulus electrodes and sense electrodes. A stimulus source provides stimuli to evoke a neural response on a neural pathway. A signal sensed at the sense electrodes includes an evoked neural response. A control unit controls the stimulus source to provide the neural stimulus according to a stimulus intensity parameter; measures an intensity of the evoked response; and completes a feedback loop using the measured intensity and controller parameters to control the stimulus intensity parameter to maintain the measured intensity at a target value. Optimal values of the controller parameters are determined from a representative value of a characteristic of the feedback loop, the representative value having been derived from data of previously programmed patients, and/or the processor is configured to determine optimal values of the one or more controller parameters from a predetermined value of an amplification parameter of the feedback loop.