Neurostimulator Therapy Mimicking Seizure Termination
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current electrical stimulation treatments for epilepsy are not optimized for individual patient outcomes, as determining the appropriate parameters for detection and therapy is complex and often requires trial and error.
Innovation Solution
A method that uses machine learning to identify a canonical seizure termination pattern from a patient's electrographic data, allowing for the generation of a stimulation therapy that mimics this pattern to terminate seizures effectively.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If current electrical stimulation treatments are used with standard programming approaches, then therapy can be delivered to patients, but the parameters for detection and therapy are not optimized for individual patient outcomes and require complex trial and error
Solution Approach 1:
The system automatically identifies canonical seizure termination patterns from the patient's own electrographic data and uses these patterns to program personalized stimulation parameters. The device self-configures by extracting features from recorded seizures and translating them into optimized therapy parameters, eliminating the need for manual trial-and-error programming by clinicians.
Solution Approach 2:
The system performs preliminary analysis of the patient's electrographic data during a programming phase to identify canonical seizure termination patterns before actual therapy delivery. This preliminary characterization of the patient's specific seizure patterns enables subsequent optimized therapy programming without requiring complex real-time adjustments.
2Ease of operation
If standardized stimulation parameters are used across patients, then device operation is simplified, but individual patient outcomes are not optimized
Solution Approach 1:
The system extracts local characteristics specific to each patient's seizure termination patterns, such as amplitude envelopes, frequency content, and temporal evolution of their own seizures. These locally-specific features are then used to program personalized stimulation parameters that match the individual patient's neural dynamics, rather than applying universal standardized parameters.
Solution Approach 2:
The system dynamically adjusts stimulation parameters based on the identified canonical patterns from each patient's data. Parameters such as pulse amplitude, width, frequency, and timing are optimized to match the specific temporal and spectral characteristics of the patient's seizure termination patterns, enabling personalized therapy.
3Reliability
If trial and error programming is used to optimize parameters, then individual patient outcomes may be improved, but time and complexity increase
Solution Approach 1:
The system performs comprehensive analysis of the patient's electrographic data in advance to identify canonical seizure termination patterns and pre-determine optimized stimulation parameters. This preliminary programming phase eliminates the need for time-consuming trial-and-error adjustments during clinical follow-up visits.
Solution Approach 2:
The system uses feedback from the patient's recorded seizure data to automatically adjust and optimize parameters. By analyzing the characteristics of actual seizures and their terminations, the system iteratively refines the canonical patterns and corresponding stimulation parameters, replacing manual trial-and-error with data-driven optimization.
Data Source
AI summary
Systems, methods, and devices for automatic generation of a stimulation therapy that mimics electrographic activity in the brain at natural seizure termination define a stimulation therapy to be generated by an implanted component of a medical device system and delivered to a subject through identifying data characterizing a patient's seizures, especially at termination. A machine learning model identifies the seizures or seizure types from which to establish a canonical seizure or seizure type, and an algorithm translates the canonical seizure or seizure type into data that can be used to characterize a stimulation therapy. The systems, methods, and devices, include those configured to deliver the stimulation therapy that emulates the canonical seizure or seizure type when the seizure is detected, with the aim of terminating the seizure sooner than it would terminate without intervention.