Automated Electrical Stimulation Programming With Closed-Loop Sensing
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Solution Overview
Problem
Conventional methods for programming implantable electrical stimulation systems are time-consuming and inefficient, requiring manual trial and error to determine optimal stimulation parameters, which can lead to lengthy processes and potential side effects.
Innovation Solution
An automated or partially automated system that sequentially tests multiple sets of stimulation parameters, senses responses using sensors, and selects optimal parameters based on therapeutic and side effects, allowing for fine-tuning by clinicians.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual trial and error methods are used to program stimulation parameters, then clinicians can determine optimal parameters through direct observation, but the process becomes time-consuming and inefficient
Solution Approach 1:
The system performs preliminary automated testing of multiple stimulation parameter sets before final clinical selection. The processor automatically cycles through candidate parameters, senses physiological responses, and pre-ranks options, so that when the clinician reviews the results, the most promising parameters are already identified, significantly reducing the time needed for final optimization.
Solution Approach 2:
The system implements closed-loop feedback by continuously sensing physiological responses (such as local field potentials or neural activity) during automated parameter testing. This real-time feedback allows the system to evaluate parameter effectiveness objectively and guide the selection process, replacing time-consuming manual trial-and-error with automated response-based optimization.
2Reliability
If extensive manual testing of stimulation parameters is performed, then optimal therapeutic effects can be achieved, but the risk of side effects increases due to prolonged exposure to suboptimal parameters
Solution Approach 1:
The system performs preliminary automated screening of multiple parameter sets to identify promising candidates before committing to extended testing. By pre-evaluating parameters using automated sensing and objective response metrics, the system能够快速 eliminate suboptimal options that might cause side effects, while preserving thorough evaluation of potentially effective parameters.
Solution Approach 2:
The system performs self-evaluation by automatically sensing physiological responses and determining parameter effectiveness without requiring prolonged manual intervention. The processor autonomously cycles through parameters, measures outcomes, and identifies optimal settings, reducing the time tissue is exposed to ineffective or harmful stimulation levels.
3Productivity
If automated parameter selection is implemented, then programming time is reduced and efficiency is improved, but device complexity increases
Solution Approach 1:
The system achieves automation by integrating multiple functions into existing implantable components. The same electrodes used for stimulation also serve as sensors for detecting physiological responses. The processor leverages existing communication interfaces and power management systems, avoiding the need for entirely new hardware and minimizing the increase in device complexity while still delivering automated parameter optimization.
4Measurement precision
If multiple sets of stimulation parameters are tested sequentially, then optimal parameters can be identified more accurately, but the duration of the programming process increases
Solution Approach 1:
The system employs periodic cycling through candidate parameter sets in an automated sequence. Rather than testing parameters randomly or through lengthy manual adjustment, the processor systematically cycles through pre-defined parameter combinations, senses responses at each step, and accumulates data to identify the optimal set. This structured periodic approach maintains thorough evaluation while minimizing total programming time.
Data Source
AI summary
A method for automating selection of stimulation parameters for a stimulation device implanted in a patient includes setting, by a user, at least one limit on each of at least one stimulation parameter and performing, automatically using at least one processor, the following actions for each of a plurality of sets of the stimulation parameters constrained by the at least one limit: stimulating the patient, by the stimulation device, using the set of stimulation parameters, sensing one or more effects arising in response to the stimulation, and updating, by the at least one processor, a collection of the effects and sets of stimulation parameters with the one or more effects and the set of stimulation parameters. The method further includes selecting, by the processor, one of the sets of stimulation parameters based on the effects.