Patient-Specific Neuromodulation Using Noise-Resistant Posture Feedback
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Solution Overview
Problem
Neurostimulation systems face challenges in maintaining effectiveness and patient comfort due to changes in posture, which affect the thickness of cerebrospinal fluid between electrodes and the target site, leading to inadequate or excessive stimulation.
Innovation Solution
A system that uses sensed electrical activity, such as electrospinograms, to dynamically adjust stimulation parameters through mathematical or statistical modeling, incorporating Kalman filters and Hidden Markov Models, to minimize noise and optimize neuromodulation based on patient-specific risk parameters and posture changes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If stimulation parameters are adjusted to compensate for posture changes, then effectiveness of neuromodulation is improved, but system complexity increases due to need for real-time sensing and dynamic adjustment mechanisms
Solution Approach 1:
The system employs a feedback mechanism where electrical activity from the spinal cord is sensed and used to dynamically adjust stimulation parameters. The stimulation control circuit continuously monitors the electrical activity and modifies stimulation intensity based on the sensed responses, creating a closed-loop control system that adapts to posture changes and maintains therapeutic effectiveness.
Solution Approach 2:
The system performs self-adjustment by automatically modifying stimulation parameters based on sensed electrical activity without requiring external intervention. The training module determines relationships between electrical activity and optimal stimulation intensity, enabling the system to self-optimize therapy delivery in response to changing patient conditions.
2Adaptability or versatility
If real-time sensing of electrical activity is implemented, then patient-specific adjustments are improved, but measurement precision is degraded due to noise from posture changes
Solution Approach 1:
The system performs preliminary training to establish relationships between electrical activity features and optimal stimulation parameters before real-time operation. During this training phase, the system collects data across various postures and stimulation levels to build a model that can later be applied in real-time, reducing the impact of noise during actual therapy delivery.
Solution Approach 2:
The system changes parameters of the sensed electrical activity signals by extracting specific features and transforming them into a form that is more robust to noise. The feature extraction module identifies relevant characteristics of the electrical activity that correlate with therapeutic response, filtering out noise components associated with posture changes.
3Reliability
If stimulation intensity is increased to maintain effectiveness during posture changes, then pain relief is improved, but patient discomfort increases due to excessive stimulation
Solution Approach 1:
The system uses feedback from sensed electrical activity to precisely control stimulation intensity, delivering only the amount of stimulation needed to achieve therapeutic effect. By monitoring the spinal cord's electrical responses in real-time, the system adjusts stimulation to maintain effectiveness while avoiding excessive intensity that would cause discomfort.
Solution Approach 2:
The system applies partial action by delivering stimulation at the minimum effective intensity rather than maximum intensity. Through continuous monitoring and adjustment based on sensed electrical activity, the system provides just enough stimulation to achieve pain relief without the excessive action that would cause discomfort, optimizing the balance between effectiveness and patient comfort.
Data Source
AI summary
An example method for delivering neurostimulation energy may include performing a training procedure by delivering the neurostimulation energy to a neural target of the patient when the patient is at one or more postures. Electrical activity is sensed from the spinal cord, such as an electrospinogram (ESG). A relationship is determined between the sensed electrical activity and neurostimulation intensity that reduces influence of noise in the sensed electrical activity caused by dynamically changing posture of the patient using mathematical or statistical modeling of the extracted features. Stimulation parameters are modulated according to the determined relationship.