Closed-Loop DBS Optimization With Wearable Inertial Sensors

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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) to neuromodulation systems, incorporating conformal wearable and wireless inertial sensor systems and machine learning, to quantify tremor power and deep brain stimulation power, and using optimization algorithms to determine optimal parameter configurations for minimal tremor power with minimal electrical power consumption.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Productivity

If manual optimization of deep brain stimulation parameters is used, then treatment efficacy can be achieved, but the process takes months to establish optimal configuration

Engineering Contradiction:

Improveoptimization speedVSAvoidtime to achieve optimal configuration

Core Design Contradiction:

ProductivityVSLoss of time

Solution Approach 1:

The patent implements a closed-loop optimization system where inertial sensors continuously monitor tremor response to DBS parameters, providing real-time feedback to an optimization algorithm. This feedback mechanism enables automated adjustment of stimulation parameters based on actual patient response, dramatically reducing the time required to achieve optimal configuration compared to manual trial-and-error approaches.

Inventive Principle:

Principle #23Feedback

Solution Approach 2:

The system enables self-service optimization by using the patient's own tremor data collected by wearable inertial sensors to automatically tune the DBS parameters. The optimization algorithm processes sensor feedback and autonomously adjusts parameters without requiring continuous manual intervention from clinicians, transforming a time-consuming manual process into an automated self-optimizing system.

Inventive Principle:

Principle #25Self-service

2Reliability

If higher deep brain stimulation amplitude is applied, then tremor suppression improves, but electrical power consumption increases

Engineering Contradiction:

Improvetremor suppression efficacyVSAvoidelectrical power consumption

Core Design Contradiction:

ReliabilityVSUse of energy by moving object

Solution Approach 1:

The patent dynamically adjusts multiple DBS parameters (amplitude, frequency, pulse width) based on real-time tremor monitoring data. Instead of using a fixed high amplitude setting, the system optimizes the combination of parameters to achieve effective tremor suppression while minimizing overall power consumption. The optimization algorithm identifies the most efficient parameter set that provides sufficient therapeutic effect with lowest energy use.

Inventive Principle:

Principle #35Parameter changes

Solution Approach 2:

The system applies partial stimulation by adjusting parameters to the minimum effective level rather than using maximum amplitude continuously. The optimization algorithm determines the precise parameter settings needed for effective tremor control, avoiding excessive energy consumption by applying only the necessary stimulation intensity required for therapeutic effect.

Inventive Principle:

Principle #16Partial or excessive action

3Duration of action of moving object

If extended battery life is achieved through reduced power usage, then device duration improves, but tremor suppression may be compromised

Engineering Contradiction:

Improvebattery lifeVSAvoidtremor suppression efficacy

Core Design Contradiction:

Duration of action of moving objectVSReliability

Solution Approach 1:

The patent implements dynamic parameter adjustment that adapts stimulation settings in real-time based on tremor severity and patient response. The system can modulate amplitude, frequency, and pulse width parameters dynamically to maintain effective tremor suppression while optimizing power consumption. This dynamic approach allows the system to extend battery life by reducing average power usage without compromising therapeutic efficacy, as parameters are continuously optimized to match actual clinical needs.

Inventive Principle:

Principle #15Dynamics

Data Source

PatentUS12415076B1Multidisciplinary design optimization of neuromodulation systems

Publication Date: 2025.09.16 LEMOYNE ROBERT

AI summary

A system that 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 by automating 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.