Spinal Cord Stimulation Pattern Prediction for Closed-Loop Pain Therapy
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Solution Overview
Problem
Existing neurostimulation systems, particularly closed-loop spinal cord stimulation (SCS) systems, rely on a 'guess-and-check' approach to select stimulation parameters, lacking a well-defined method for identifying optimal therapy settings, leading to inefficiencies in treating chronic pain.
Innovation Solution
A method involving neural sensing to record patient activity, computational modeling to assess the impact of different temporal stimulation patterns, and selecting an optimal pattern based on neural responses, with a closed-loop controller for dynamic adjustment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If a guess-and-check approach is used to select stimulation parameters, then various parameter sets can be tested, but the process is inefficient and lacks a well-defined method for identifying optimal therapy settings
Solution Approach 1:
The patent applies parameter changes by systematically varying stimulation parameters (amplitude, pulse width, frequency, contact configuration) based on computational model predictions rather than random guess-and-check. The model guides which parameters to test and in what order, transforming the盲目 parameter selection into a directed search process that improves efficiency while managing complexity.
Solution Approach 2:
The patent replaces the manual trial-and-error mechanical process with a computational modeling system that uses algorithms to predict optimal parameters. This substitution of computational methods for manual adjustment significantly improves productivity by eliminating the inefficiency of systematic but unguided parameter testing.
2Reliability
If multiple stimulation parameters are independently tuned to achieve optimal therapy, then therapeutic efficacy can be improved, but the number of parameters to tune increases complexity
Solution Approach 1:
The computational model performs preliminary action by predicting which parameter combinations are likely to be optimal before actual stimulation begins. This pre-computation guides the parameter tuning process, allowing clinicians to focus on a reduced set of promising parameters rather than exhaustively testing all possible combinations, thus maintaining therapeutic efficacy while reducing complexity.
Solution Approach 2:
The computational modeling system acts as an intermediary between the clinician and the multiple stimulation parameters. Rather than directly managing the complexity of tuning amplitude, pulse width, frequency, and contact configuration simultaneously, the model serves as a mediator that processes these parameters and recommends optimal settings, reducing the perceived complexity for the user.
3Adaptability or versatility
If trial-and-error approach is used to determine which stimulation waveform provides the most pain relief, then individual patient response can be assessed, but time is lost in the selection process
Solution Approach 1:
The computational model performs preliminary assessments of different waveform types (tonic, burst, etc.) based on patient-specific neural activity patterns before actual therapy begins. This allows the system to predict which waveform is most likely to be effective for each individual patient, reducing the time needed for trial-and-error assessment while maintaining adaptability to individual responses.
Solution Approach 2:
The system uses feedback from recorded neural activity to refine waveform selection. By continuously monitoring patient response and feeding this information back into the computational model, the system can rapidly adapt waveform parameters to achieve optimal individualized therapy, significantly reducing the time compared to traditional trial-and-error methods while maintaining high adaptability.
Data Source
AI summary
In one embodiment, the present disclosure is directed to a method for providing a neural stimulation therapy to treat chronic pain of a patient. The method comprises: recording, using a neural sensing system, neural activity of the patient at one or more sites within the nervous system of the patient related to the chronic pain of the patient, modifying a computational neural modeling system to model the sensed neural activity of the patient; computing a respective neural response of the patient for each of a plurality of different temporal stimulation patterns using the modified computational neural modeling system; selecting, based on the respective neural responses, one of the plurality of temporal stimulation patterns; and programming an implantable stimulation system to provide the selected one of the plurality of temporal stimulation patterns to the patient to treat the chronic pain of the patient.