Closed-Loop Stimulation Parameter Optimization Using Machine Learning
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Solution Overview
Problem
Current implantable electrical stimulation systems require manual and time-consuming programming processes, which can be inefficient and may not always achieve optimal therapeutic responses due to the complexity of parameter settings and the need for clinician intervention.
Innovation Solution
A closed-loop programming system utilizing machine learning engines to automatically generate and adjust stimulation parameter values for an implantable pulse generator, incorporating feedback from sensors to iteratively refine settings until a therapeutic response is achieved within a designated tolerance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If manual programming methods are used, then clinician control over stimulation parameters is maintained, but programming time and complexity increase significantly
Solution Approach 1:
The system implements a closed-loop feedback mechanism where clinical response data is continuously fed back to the machine learning model. The model uses this feedback to iteratively refine and adjust stimulation parameter recommendations, creating an adaptive programming process that improves efficiency while maintaining therapeutic effectiveness.
Solution Approach 2:
The machine learning system performs automated parameter optimization without requiring manual intervention for each adjustment. The system autonomously processes clinical responses, updates its models, and generates optimized parameter sets, reducing the clinician's workload from manual trial-and-error programming to supervisory review.
2Productivity
If automated machine learning programming is implemented, then programming efficiency improves, but system complexity increases
Solution Approach 1:
The machine learning model acts as an intermediary between the clinician's therapeutic goals and the complex stimulation parameter space. Rather than requiring direct manual adjustment of multiple parameters, the system uses the ML model as a mediator that translates clinical objectives into optimized parameter recommendations, simplifying the user interface while maintaining sophisticated optimization capabilities.
Solution Approach 2:
The patent replaces manual mechanical parameter adjustment with an automated computational system. The machine learning algorithms substitute for the clinician's manual trial-and-error programming process, using computational intelligence to navigate the complex parameter space and generate optimized settings, thereby increasing productivity while concentrating complexity in the automated system rather than the user interface.
3Measurement precision
If iterative parameter optimization is performed, then therapeutic response accuracy improves, but computational resources and time consumption increase
Solution Approach 1:
The system implements iterative optimization but stops when predefined convergence criteria are met or when sufficient accuracy is achieved. Rather than continuously optimizing indefinitely, the system performs partial iterations until the marginal benefit of additional optimization diminishes, balancing therapeutic response accuracy with computational resource consumption.
Data Source
AI summary
A method or system for facilitating the determining and setting of stimulation parameters for programming an electrical stimulation system using closed loop programming is provided. For example, pulse generator feedback logic is executed by a processor to interface with control instructions of an implantable pulse generator by incorporating one or more machine learning engines to automatically generate a proposed set of stimulation parameter values that each affect a stimulation aspect of the implantable pulse generator, receive one or more clinical responses and automatically generate a revised set of values taking into account the received clinical responses, and repeating the automated receiving of a clinical response and adjusting the stimulation parameter values taking the clinical response into account, until or unless a stop condition is reach or the a therapeutic response is indicated within a designated tolerance.