Implanted DBS Controller With Patient-Feedback Parameter Adjustment
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Solution Overview
Problem
The process of optimizing stimulation parameters for deep brain stimulation (DBS) is time-consuming and often results in suboptimal outcomes, requiring frequent clinician intervention, which is costly and inconvenient for patients.
Innovation Solution
An implanted controller adjusts therapeutic stimulation based on patient feedback without external input, using a semi-autonomous system that includes a processor to generate and adjust stimulation signals, incorporating machine learning algorithms to determine optimal parameters.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If stimulation parameters are adjusted manually by clinicians during fitting sessions, then therapeutic efficacy can be optimized, but the process becomes time-consuming and requires frequent patient visits to clinics
Solution Approach 1:
The implanted controller autonomously adjusts stimulation parameters by processing patient feedback signals and executing adjustment algorithms without requiring external clinician intervention. The system performs self-diagnosis and self-adjustment, enabling patients to optimize their own therapy at home through simple feedback input rather than requiring time-consuming clinic visits
Solution Approach 2:
The system implements a closed-loop feedback mechanism where patient responses to stimulation are continuously monitored and used to automatically adjust parameters. The controller receives feedback signals from the patient indicating therapeutic effect, processes this information through adjustment algorithms, and modifies stimulation parameters in real-time to maintain optimal therapy
2Reliability
If stimulation parameters are adjusted frequently to maintain optimal therapy, then therapeutic efficacy is improved, but the cost and inconvenience of frequent clinic visits increase
Solution Approach 1:
The system enables patients to perform parameter optimization at home without traveling to clinics. The implanted controller autonomously processes patient feedback and adjusts parameters, transforming a previously clinic-dependent process into a self-service home-based therapy optimization that maintains efficacy while dramatically improving convenience
Solution Approach 2:
The implanted controller acts as an intermediary between the patient and the stimulation electrodes, automatically translating simple patient feedback into complex parameter adjustments. This intermediary function eliminates the need for direct clinician-patient interaction during routine adjustments, allowing frequent optimization without frequent visits
3Adaptability or versatility
If manual fitting processes are used to determine optimal parameters, then individualized therapy can be achieved, but the process is complex and frequently results in suboptimal outcomes
Solution Approach 1:
The system uses automated feedback processing to objectively determine optimal parameters based on actual patient response rather than subjective clinician judgment. The controller continuously monitors therapeutic effect through patient feedback signals and uses adjustment algorithms to systematically optimize parameters, reducing the complexity and subjectivity of manual fitting while maintaining individualized therapy
Solution Approach 2:
The system automatically varies stimulation parameters (amplitude, pulse width, frequency, electrode selection) based on processed patient feedback. The adjustment algorithms systematically explore parameter space and identify optimal combinations tailored to each patient's unique response characteristics, achieving individualized therapy through automated parameter optimization rather than manual trial-and-error
Data Source
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AI summary
Some embodiments relate to a method of adjusting therapeutic stimulation from a therapeutic stimulation system. The method comprising: providing therapeutic stimulation based on a plurality of stimulation parameters to a patient with an implanted controller and at least two electrodes; receiving data indicative of feedback associated with a patient rating of the therapeutic stimulation via a patient input device; automatically adjusting, with the implanted controller, at least one of the plurality of stimulation parameters; and providing adjusted therapeutic stimulation based on the adjusted at least one stimulation parameter to the patient with the controller and the at least two electrodes. The method further comprises receiving additional data indicative of feedback associated with another patient rating of the adjusted therapeutic stimulation; and executing a machine learning algorithm based on the stimulation parameters and received data indicative of feedback, to determine preferred stimulation parameters.