Adaptive Brain Stimulation System for Dynamic Patient State Adjustment
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Solution Overview
Problem
Existing deep brain stimulation (DBS) therapies struggle to adapt to changing patient conditions over time, requiring frequent clinic visits to adjust threshold values, and often rely on one-size-fits-all algorithms that may not be optimal for individual patients.
Innovation Solution
The development of an adaptive brain stimulation system that automatically adjusts stimulation waveforms in real-time using a closed-loop control system, incorporating sensors for local field potentials and movement, and reconfiguring itself based on performance measures to optimize therapy delivery.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional continuous DBS therapy is provided, then symptom alleviation is achieved, but adaptability to changing patient conditions deteriorates
Solution Approach 1:
The DBS system transitions from static continuous stimulation to dynamic adaptive stimulation by continuously monitoring physiological signals (local field potentials, movement sensors) and adjusting stimulation parameters in real-time based on detected brain states and patient responses, enabling the system to adapt to changing conditions while maintaining symptom control
Solution Approach 2:
The system implements closed-loop feedback control by sensing physiological parameters, comparing them against threshold values, and adjusting stimulation delivery accordingly. The feedback mechanism allows the system to detect changes in patient state and modify therapy parameters to maintain optimal symptom management
2Adaptability or versatility
If threshold-adjusted therapy is implemented, then adaptability improves, but device complexity and need for clinic visits increases
Solution Approach 1:
The system performs self-adjustment of stimulation parameters by automatically detecting patient state changes through physiological sensors and modifying therapy without external intervention. This self-service capability reduces the need for frequent clinic visits and manual threshold adjustments while maintaining adaptability
Solution Approach 2:
The system pre-programs multiple stimulation thresholds and corresponding therapy parameters, allowing it to automatically select appropriate settings based on detected physiological states. This preliminary configuration enables rapid adaptation without requiring complex real-time decision-making or frequent external adjustments
3Device complexity
If one-size-fits-all algorithms are used, then device complexity is reduced, but manufacturing precision of therapy delivery deteriorates
Solution Approach 1:
The system tailors stimulation parameters to local patient-specific characteristics by monitoring individual physiological signals and adjusting therapy based on detected brain states. This localized adaptation approach delivers precise, personalized therapy while using relatively simple algorithmic rules to determine parameter adjustments
Data Source
AI summary
Systems and methods that automatically adjust, or adapt, stimulation waveforms delivered to brain structures. Closed loop system embodiments can automatically be reconfigured into a more suitable closed loop control system in response to measures of control system performance. Measures can be internal performance characteristics of the adaptive control system or external inputs provided by another subsystem. As these measures change in time, the robust adaptive system changes in response.