Adaptive Dual Controller for Medical Devices Using Bayesian Optimization
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Solution Overview
Problem
Current controllable medical device technologies do not readily adapt to the changing needs of patients, as they rely on static parameter settings that do not account for real-time patient feedback and preferences.
Innovation Solution
An adaptive dual controller using Bayesian optimization is implemented, which receives feedback data from patients, generates a posterior distribution, estimates an acquisition function, and updates control parameter settings in real-time to optimize medical device performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If static parameter settings are used in medical devices, then device simplicity is maintained, but adaptability to changing patient needs deteriorates
Solution Approach 1:
The patent implements dynamic parameter adjustment by transitioning from static control settings to real-time adaptive control. The system continuously updates control parameters based on incoming patient feedback data, enabling the medical device to adapt its behavior dynamically to changing patient needs while maintaining a relatively simple overall architecture through automated adaptation.
Solution Approach 2:
The patent employs feedback mechanisms by continuously monitoring patient responses and using this information to adjust control parameters. The system processes patient feedback data (such as symptom reports or physiological measurements) and automatically modifies treatment parameters, creating a closed-loop control system that improves adaptability without requiring complex manual reprogramming.
2Productivity
If manual reprogramming visits are required, then control precision is maintained through clinician expertise, but time efficiency and patient convenience deteriorate
Solution Approach 1:
The patent implements self-service functionality by enabling the medical device to automatically adjust its own control parameters based on patient feedback. The system performs self-diagnosis and self-tuning by processing patient responses and autonomously modifying treatment parameters, eliminating the need for frequent manual reprogramming visits and significantly reducing the time loss between parameter updates.
Solution Approach 2:
The patent ensures continuous optimization of treatment parameters by implementing real-time or near-real-time adjustment capabilities. Instead of discrete periodic updates during scheduled visits, the system continuously monitors patient feedback and makes ongoing parameter adjustments, maintaining optimal treatment efficacy without interruption and maximizing productivity.
3Reliability
If patient feedback is continuously monitored, then treatment efficacy is improved through real-time adaptation, but device complexity and data processing requirements increase
Solution Approach 1:
The patent replaces complex mechanical or manual data processing systems with computational algorithms. Instead of using elaborate hardware systems to process patient feedback, the invention employs software-based Bayesian optimization and machine learning algorithms that can be implemented in standard processors, achieving high treatment efficacy while keeping the physical device architecture relatively simple.
Solution Approach 2:
The patent focuses on adjusting a limited set of critical control parameters rather than comprehensively analyzing all possible patient feedback variables. By identifying and optimizing key parameters that have the greatest impact on treatment efficacy, the system achieves reliable results while minimizing data processing complexity and computational requirements.
4Adaptability or versatility
If Bayesian optimization is implemented for real-time control, then adaptability and treatment efficacy improve, but computational requirements and energy consumption increase
Solution Approach 1:
The patent implements partial Bayesian optimization by focusing computational resources on optimizing only the most critical control parameters rather than performing exhaustive optimization of all parameters. This selective approach maintains real-time adaptability for the most important treatment aspects while significantly reducing overall computational energy consumption compared to complete system optimization.
Data Source
AI summary
Systems and methods for adaptively controlling an electrical stimulation device, such as a closed-loop stimulation device, based in part on a Bayesian optimization of the operational parameters of the device are described. An adaptive dual control of the stimulation device can be provided. In a first control loop parameters are extracted from signals recorded from the subject by the stimulation device, and in a second control loop a Bayesian optimization is implemented with a hardware processor and memory to compute updated operational parameters for the stimulation device. As noted, the stimulation device is an electrical stimulation device, and may be a closed-loop stimulation device. Such devices can be used for deep brain stimulation (“DBS”), cardiac resynchronization therapy (“CRT”), and other electrophysiological stimulation applications.