Spinal Cord Stimulation Feedback Control Using ECAP Morphology
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing spinal cord stimulation systems lack effective closed-loop control mechanisms to adapt stimulation parameters based on real-time neural responses, leading to suboptimal therapeutic outcomes due to variations in tissue environment, such as cerebrospinal fluid thickness.
Innovation Solution
Incorporating electrodes for both stimulation and sensing capabilities to monitor neural responses, utilizing feedback control algorithms like Kalman filters and PID models to adjust stimulation parameters based on sensed neural features, such as amplitude and shape, to maintain optimal therapeutic effects.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If spinal cord stimulation systems use fixed stimulation parameters, then the device complexity is reduced, but the therapeutic effectiveness deteriorates due to inability to adapt to tissue environment variations
Solution Approach 1:
The patent implements closed-loop control by sensing neural responses (ECAPs) and using feedback control algorithms to dynamically adjust stimulation parameters. The system monitors neural response features and automatically modifies stimulation amplitude, pulse width, or frequency to maintain optimal therapeutic effect, resolving the contradiction between fixed parameters and adaptive effectiveness.
Solution Approach 2:
The patent transitions from static stimulation parameters to dynamic parameter adjustment by continuously adapting stimulation settings based on real-time neural response measurements. This allows the system to respond to tissue environment variations such as changes in cerebrospinal fluid thickness, electrode-tissue distance, or neural excitability, thereby maintaining reliable therapeutic effectiveness.
2Measurement precision
If the system continuously monitors neural responses and adjusts stimulation parameters, then the therapeutic precision is improved, but the energy consumption increases
Solution Approach 1:
The patent employs periodic sensing and adjustment cycles rather than continuous monitoring. The system senses neural responses at intervals, processes the data through feedback algorithms, and adjusts parameters periodically, which reduces energy consumption compared to continuous real-time monitoring while maintaining adequate measurement precision for effective closed-loop control.
3Measurement precision
If the system uses multiple neural response features for control decisions, then the control accuracy is improved, but the device complexity increases
Solution Approach 1:
The patent applies different processing strategies to different neural response features based on their sensitivity characteristics. Amplitude-based features are used for detecting large changes in tissue environment, while morphology-based features are used for finer adjustments. This localized approach to feature utilization improves control accuracy without requiring complex processing of all possible features simultaneously.
Data Source
Figure 1~2B
Figure 3
Figure 4
AI summary
Methods and systems for using sensed neural responses for informing aspects of stimulation therapy are disclosed. For example, features of evoked neural responses, such as evoked compound action potentials (ECAPs) can be used for closed-loop feedback control of stimulation parameters. Aspects of the disclosed methods and systems can differentiate between changes in the sensed neural responses that are caused by the environment at stimulating electrodes and changes in the neural responses that are caused by the environment at sensing electrodes. Embodiments determine changes in the morphology of the neural responses, which morphology changes indicate a degree of change in the stimulating environment. Algorithms and systems for assigning and tracking likelihoods for underlying electrode-tissue changes based on sensed neural responses are disclosed. The feedback control modality may be updated based on such likelihoods. Also disclosed are methods and systems for determining which features of evoked neural responses are more sensitive to changes in the stimulating environment and less sensitive to changes in the sensing environment.