Closed-loop brain stimulation for dynamic seizure control
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Solution Overview
Problem
Current therapies for pharmaco-resistant seizures, such as brain electrical stimulation, lack optimization strategies to account for the complex and dynamic nature of epileptic brain activity, leading to inadequate therapeutic efficacy and adverse effects due to neglect of spatio-temporal inhomogeneity, tissue anisotropy, and circadian influences.
Innovation Solution
A method involving real-time characterization of spatio-temporal brain activity using signal processing and optimization techniques to tailor therapy parameters, such as wave rhythmicity and synchrony, to enhance therapeutic efficacy and reduce adverse effects by delivering therapies at optimal times and locations within the brain.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If brain electrical stimulation is delivered using conventional fixed parameters, then the therapy can be administered continuously and simply, but the therapeutic efficacy is insufficient due to lack of optimization for dynamic brain activity patterns
Solution Approach 1:
The patent implements dynamic therapy parameter adjustment by continuously monitoring brain activity patterns and adapting stimulation parameters in real-time. The system transitions from fixed static parameters to dynamic parameters that evolve with brain state, improving therapeutic efficacy while managing complexity through automated feedback loops.
Solution Approach 2:
The system employs closed-loop feedback by monitoring brain activity (such as EEG signals) and using this information to adjust therapy parameters. The feedback mechanism compares actual brain state with target state and modifies stimulation parameters accordingly, resolving the contradiction between simple administration and effective optimization.
2Reliability
If therapy parameters are optimized in real-time based on spatio-temporal brain activity, then therapeutic efficacy improves, but the system complexity and computational requirements increase significantly
Solution Approach 1:
The patent segments the brain into multiple regions and processes signals from different spatial locations independently. By dividing the complex spatio-temporal analysis into smaller regional components, the system can optimize therapy parameters for each region without overwhelming computational complexity, thereby maintaining high seizure control effectiveness.
Solution Approach 2:
The system performs preliminary characterization of brain activity patterns and pre-computes optimal therapy parameters in advance of actual seizure events. By preparing optimization parameters beforehand based on baseline brain activity, the system reduces real-time computational burden while maintaining effective seizure control.
3Ease of operation
If fixed-dose therapy is administered, then the treatment protocol is simple to implement, but adverse effects occur due to lack of personalization for individual brain activity patterns
Solution Approach 1:
The patent implements personalized therapy by dynamically changing stimulation parameters (amplitude, frequency, pulse width) based on individual patient brain activity patterns. The system adapts parameters to match each patient's unique neurophysiology, reducing adverse effects while maintaining ease of operation through automated parameter adjustment rather than manual customization.
Solution Approach 2:
The therapy system performs self-adjustment by automatically monitoring brain activity and modifying its own parameters without external intervention. This self-service capability personalizes treatment for each individual's brain patterns while keeping the operation simple, as the system autonomously optimizes parameters without requiring complex user input or manual recalibration.
4Adaptability or versatility
If continuous brain monitoring is performed to optimize therapy delivery, then personalized treatment is achieved, but energy consumption and device complexity increase
Solution Approach 1:
The patent implements periodic sampling of brain activity rather than truly continuous monitoring. The system monitors brain signals at strategically selected time intervals and uses this periodic data to adjust therapy parameters. This approach achieves personalized adaptation while significantly reducing energy consumption compared to continuous high-rate sampling.
Solution Approach 2:
The system performs monitoring and optimization at selective moments rather than continuously at full capacity. By using partial monitoring (sampling key events and transitions) rather than exhaustive continuous analysis, the system achieves sufficient personalization while conserving energy resources in implantable devices.
Data Source
AI summary
A method for assessment, optimization and logging of the effects of a therapy for a medical condition, including (a) receiving into a signal processor input signals indicative of the subject's brain activity; (b) characterizing the spatio-temporal behavior of the brain activity using the signals; (c) delivering a therapy to a target tissue of the subject; (d) characterizing the spatio-temporal effect of the therapy on the brain activity; (e) in response to the characterizing, optimizing at least one parameter of the therapy if the brain activity has not been satisfactorily modified and/or has been adversely modified by the therapy; (f) characterizing the spatio-temporal effect of the at least one optimized parameter; and (g) logging to memory the at least one optimized parameter. A computer readable program storage unit encoded with instructions that, when executed by a computer, performs the method.