Neurostimulation Parameter Tuning Using Patient Feedback Loops
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Solution Overview
Problem
Conventional bioelectronic devices require tedious and time-consuming trial-and-error approaches to configure stimulation settings for each patient, often taking months to establish therapeutic effects, with inefficient manual adjustments by nurses and suboptimal stimulation due to lengthy follow-up visits.
Innovation Solution
A self-programming stimulation settings system using a computing device, such as a smartphone or tablet, that automatically configures electrode arrays based on patient feedback, adjusting parameters like frequency, pulse width, and amplitude, and captures feedback through imaging and biomarkers to optimize therapy settings.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual trial-and-error configuration is used to determine optimal stimulation settings, then therapeutic effectiveness may be achieved through iterative adjustments, but the process takes months and requires numerous follow-up visits
Solution Approach 1:
The system enables patients to independently perform stimulation configuration and optimization at home without requiring nurse or clinician involvement. The automated system guides patients through the process, allowing them to self-adjust stimulation parameters based on real-time feedback, thereby eliminating the need for repeated clinical follow-up visits while maintaining therapeutic effectiveness
Solution Approach 2:
The system incorporates real-time feedback mechanisms where patient responses to stimulation are immediately captured and used to automatically adjust stimulation parameters. This closed-loop feedback system enables rapid convergence to optimal settings without the months-long iterative process traditionally required, significantly reducing setup time while ensuring reliable therapeutic outcomes
2Adaptability or versatility
If manual configuration by nurses is used, then stimulation settings can be adjusted, but the process is tedious and requires weeks or months of follow-up visits
Solution Approach 1:
The system transfers the configuration task from healthcare professionals to the patient themselves. An automated guided process walks patients through stimulation optimization, making the complex task simple and intuitive. This eliminates the burden on nurses while maintaining high adaptability to individual patient needs through real-time feedback-driven adjustments
Solution Approach 2:
The system introduces an automated software intermediary that mediates between the patient and the complex stimulation configuration process. This intermediary translates patient feedback into appropriate parameter adjustments, shielding patients from complexity while achieving highly customized stimulation settings tailored to individual therapeutic needs
3Measurement precision
If traditional follow-up visits are scheduled to iteratively adapt therapy, then optimal stimulation can be achieved, but patients must attend weeks or months of appointments
Solution Approach 1:
The system implements continuous real-time feedback collection during daily use, capturing patient responses immediately rather than waiting for scheduled visits. This enables precise measurement of stimulation effects and rapid parameter optimization, achieving the same level of measurement precision as manual visits but at much higher speed through automated daily adjustments
Solution Approach 2:
The system enables continuous stimulation optimization throughout the day as patients use the device, rather than relying on discrete periodic visits. This continuous action allows the system to progressively refine stimulation parameters in real-time, dramatically accelerating therapy establishment while maintaining precise optimization that would traditionally require multiple appointments
Data Source
AI summary
A method includes configuring, by a computing device, stimulation settings for each electrode in an at least one electrode array in physical contact with a patient, the stimulation settings having adjustable parameters comprising frequency, pulse width, and amplitude, obtaining, by the computing device, feedback information from the patient, and automatically adjusting, by the computing device, at least one of the adjustable parameters based on the feedback information from the patient.