Closed-Loop Deep Brain Stimulation Parameter Optimization
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Solution Overview
Problem
Existing neuromodulation systems, such as deep brain stimulation for treating Parkinson's disease and essential tremor, face challenges in achieving optimal parameter configurations, which can take months to establish, and there is a need for a more efficient and automated method to optimize these configurations.
Innovation Solution
Applying multidisciplinary design optimization (MDO) using wearable inertial sensors and machine learning to quantify tremor power and deep brain stimulation power, optimizing parameters like amplitude, pulse width, and frequency to minimize tremor power while minimizing electrical power consumption.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual optimization of deep brain stimulation parameters is used, then parameter configuration can be adjusted, but the process takes months to achieve optimal configuration
Solution Approach 1:
The system implements closed-loop optimization by continuously monitoring tremor responses through wearable inertial sensors and using this feedback to automatically adjust deep brain stimulation parameters. The machine learning model processes sensor data in real-time and modifies stimulation parameters accordingly, enabling rapid iterative optimization without manual intervention.
Solution Approach 2:
The optimization system operates autonomously by automatically adjusting deep brain stimulation parameters based on real-time tremor data from wearable sensors. The machine learning model continuously learns from patient responses and self-tunes the stimulation parameters, eliminating the need for manual clinical adjustment and significantly reducing optimization time.
2Reliability
If deep brain stimulation amplitude is increased to suppress tremor, then tremor suppression improves, but electrical power consumption increases
Solution Approach 1:
The system dynamically adjusts multiple deep brain stimulation parameters including amplitude, frequency, and pulse width based on real-time tremor severity detected by inertial sensors. The machine learning model identifies optimal parameter combinations that achieve effective tremor suppression while minimizing power consumption, allowing flexible adaptation to changing patient needs.
Solution Approach 2:
The optimization system transitions from static fixed-parameter stimulation to dynamic adaptive stimulation. Parameters are continuously adjusted in real-time based on measured tremor responses, enabling the system to use minimal effective power at each moment rather than maintaining high constant power levels, thus reducing overall energy consumption.
Data Source
AI summary
The present invention amalgamates the domains of deep brain stimulation, wearable and wireless inertial sensor systems, machine learning, and multidisciplinary design optimization to achieve an optimal parameter configuration. This present invention's respective amalgamation attains a means to automate the acquisition of an optimal parameter configuration for deep brain stimulation for movement disorders, such as essential tremor and Parkinson's disease, in a closed loop context. Wearable inertial sensors provide quantified feedback of movement disorder response to a deep brain stimulation parameter configuration. Using multidisciplinary design optimization, a minimal effective power, which is derived from tremor power and deep brain stimulation power, is acquired constituting an optimal parameter configuration.