Closed-Loop Neurostimulation Recommendation System
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Solution Overview
Problem
Current neurostimulation systems face challenges in providing personalized and dynamic programming adjustments that cater to changing patient needs, as some patients or clinicians may not be able to effectively utilize closed-loop programming systems.
Innovation Solution
A system comprising processors and memory devices that utilize a recommendation model to generate customized programming settings for neurostimulation devices, allowing communication of these settings to patient devices for reconfiguration, with user interfaces for medical and patient users to activate and customize recommendations, including feedback-based adjustments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If closed-loop programming is implemented to enable personalized and automated neurostimulation adjustments, then treatment customization and efficacy are improved, but system complexity and difficulty of use increase for patients and clinicians
Solution Approach 1:
The patent introduces an external computing device (smartphone, tablet, or computer) as an intermediary between the neurostimulation system and the user. This external device handles the complex processing, machine learning algorithms, and programming adjustments, while the implanted neurostimulator remains relatively simple. The intermediary device communicates with the implant via wireless connection, enabling personalized treatment customization without increasing the complexity of the implanted device itself.
Solution Approach 2:
The system enables patients to independently manage and customize their own neurostimulation treatment through user-friendly interfaces on external devices. The machine learning algorithms automatically analyze patient feedback and physiological data to suggest optimal programming adjustments, which patients can approve with minimal clinical intervention. This self-service capability allows personalized treatment adaptation without requiring complex manual programming by clinicians.
2Adaptability or versatility
If closed-loop programming allows frequent and dynamic parameter adjustments, then treatment responsiveness to changing patient needs is improved, but ease of operation deteriorates due to the number of programming options
Solution Approach 1:
The system continuously collects feedback from patients through mobile device interfaces (pain ratings, comfort levels, activity data) and from physiological sensors (movement, heart rate, galvanic skin response). Machine learning algorithms process this feedback in real-time to automatically generate programming adjustments. This feedback loop enables dynamic parameter adaptation without requiring patients to manually navigate complex programming options, as the system learns and adjusts based on ongoing patient responses.
Solution Approach 2:
The patent replaces manual mechanical programming (physically adjusting device parameters through buttons or dials) with automated electronic/algorithmic programming. Machine learning models process patient data and automatically determine optimal stimulation parameters, substituting the need for manual intervention. This substitution maintains ease of operation while enabling frequent dynamic adjustments, as the automated system handles the complexity of parameter selection based on real-time patient needs.
3Adaptability or versatility
If more programming options and parameters are available for customization, then treatment adaptability is improved, but device complexity and time required for programming increase
Solution Approach 1:
The system performs preliminary actions by pre-processing and analyzing patient data continuously in the background using machine learning algorithms. The external computing device maintains ready-to-apply programming recommendations based on ongoing analysis of patient feedback and physiological data. When treatment adjustments are needed, the system can quickly deploy pre-calculated programming options without requiring time-consuming manual analysis or trial-and-error programming sessions, thus reducing programming time while maintaining extensive customization options.
Solution Approach 2:
The patent implements efficient parameter management by using machine learning to identify and adjust only the most critical stimulation parameters based on patient needs, rather than allowing manual adjustment of all possible parameters. The system dynamically changes parameters such as pulse amplitude, pulse width, frequency, and electrode selection based on real-time feedback, automatically prioritizing the most impactful adjustments. This approach maintains treatment adaptability while significantly reducing the time required for programming compared to manual adjustment of all available parameters.
Data Source
AI summary
Systems and methods for generating and implementing customized recommendations as part of closed-loop neurostimulation programming and device configuration approaches are disclosed. In an example, a system is configured to: identify, with a recommendation model (e.g., a closed-loop programming algorithm), a programming setting for use in a neurostimulation device that controls neurostimulation treatment of a patient; communicate, to a patient device (e.g., a patient smartphone), a command to present a recommendation to use the programming setting; and communicate, to the patient device, data values associated with the programming setting, to reconfigure the neurostimulation device according to the programming setting. In various examples, a medical user interface enables a medical user (e.g., clinician) to customize the recommendation, and a patient user interface enables a patient to view and use the recommendation.