Dueling Bandits Algorithm for Neuromodulation Waveform Selection
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Solution Overview
Problem
Current neurostimulation therapies for conditions like spinal cord injuries and chronic pain are inefficient due to the laborious process of finding optimal electrode groups and stimulation parameters, which consumes valuable clinician and patient time and does not guarantee optimal outcomes, as each patient's response varies significantly.
Innovation Solution
A dueling bandits algorithm is used to quickly converge on optimal or near-optimal complex stimulation waveforms by organizing them into correlated stimulation arms, allowing for rapid identification of effective electrode groups and stimuli parameters through a closed-loop system, potentially within seconds or minutes, compared to manual processes that take months.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual trial-and-error method is used to find optimal stimulation parameters, then comprehensive testing of electrode combinations is possible, but time consumption increases significantly (3-6 months)
Solution Approach 1:
The system implements a closed-loop feedback mechanism where patient responses to stimulation are continuously monitored and fed back to the algorithm. The dueling bandits algorithm uses this feedback to iteratively refine and converge on optimal stimulation waveforms, adjusting parameters based on real-time patient feedback rather than relying on exhaustive manual testing.
Solution Approach 2:
The system enables self-service by allowing the algorithm to autonomously identify optimal stimulation parameters without requiring extensive manual intervention. The automated dueling bandits algorithm independently tests, evaluates, and selects waveform combinations, reducing clinician time commitment from months to days or weeks while maintaining comprehensive parameter exploration.
2Manufacturing precision
If exhaustive testing of all electrode combinations is performed, then optimal therapy can be identified, but therapy time is reduced leaving less time for actual treatment
Solution Approach 1:
The system performs preliminary action by pre-defining and organizing stimulation arms with various electrode combinations and waveform parameters before patient therapy begins. The dueling bandits algorithm is pre-configured with multiple stimulation arms, allowing it to efficiently evaluate promising combinations without needing to test every possible configuration during actual therapy sessions.
Solution Approach 2:
The system applies dynamics by making the stimulation configuration adaptive and changeable during therapy. The algorithm dynamically adjusts which stimulation arms are tested and how parameters are evaluated based on real-time patient responses, allowing the system to converge on optimal settings efficiently while preserving time for actual treatment delivery.
3Ease of manufacture
If population-based models are used for therapy, then treatment can be standardized, but effectiveness decreases due to individual patient variations
Solution Approach 1:
The system implements local quality by customizing stimulation parameters and electrode configurations for each individual patient based on their specific responses. The dueling bandits algorithm evaluates and selects stimulation arms tailored to each patient's unique neural anatomy and response characteristics, rather than applying a one-size-fits-all population-based approach, thereby improving individualized treatment effectiveness.
Data Source
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AI summary
A system, method, and apparatus for identifying optimal or near optimal complex stimulation waveforms for a neurostimulator device or neuromodulation device are disclosed. An example method includes using a dueling bandits algorithm with correlation among stimulation arms to select a batch of stimulation arms for sequential application to a patient during a therapy session. Each of the stimulation arms specifies complex stimulation waveform parameter values. Feedback from applying the stimulation arms to the patient is recorded and used to update feedback reward values corresponding to at least some of the stimulation arms using a stimulation arm correlation index. A second batch of stimulations arms is selected based upon the updated feedback reward values and applied to a patient. The method is iteratively repeated over a number of therapy sessions until an optimal or near optimal batch of stimulation arms (defining complex stimulation waveforms) is determined.