Seizure Onset Zone Localization Using Inter-Ictal EEG Analysis
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Solution Overview
Problem
Current methods for localizing the seizure-onset zone in epilepsy patients are inefficient and unreliable, relying on expensive and time-consuming ictal EEG recordings, which require extended patient monitoring for seizure occurrence.
Innovation Solution
The use of inter-ictal EEG data and Bayesian filtering to classify sensor channels as normal or abnormal, leveraging repetitive abnormal events and temporally synchronized occurrences to identify epileptogenic regions, allowing for unsupervised and automatic localization of seizure-generating brain regions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If ictal EEG recordings are used to localize seizure-onset zone, then localization accuracy is improved, but monitoring duration and time consumption increase significantly
Solution Approach 1:
The patent applies preliminary action by using inter-ictal EEG data (recorded between seizures) to pre-identify candidate seizure-onset zones before actual seizures occur. The system segments inter-ictal data into epochs, computes features, and identifies abnormal channels in advance, so that when a seizure occurs, localization can be rapidly confirmed without needing to wait for multiple ictal events. This preliminary classification of channels based on inter-ictal abnormalities resolves the contradiction by providing advance information that reduces the monitoring duration needed for accurate localization.
2Reliability
If manual inspection of multiple ictal events is performed, then localization reliability is improved, but labor intensity and complexity increase
Solution Approach 1:
The patent applies self-service by implementing an automated computational system that performs the localization task without requiring manual inspection of multiple ictal events. The system automatically segments EEG data, extracts features, classifies channels as normal or abnormal using computational algorithms, and generates localization results. This automated process maintains reliability through systematic analysis while eliminating the labor intensity and subjective variability associated with manual inspection, thus resolving the contradiction between reliability and process complexity.
Solution Approach 2:
The patent replaces the mechanical/manual system of visual inspection with an automated computational system. Instead of neurologists manually examining EEG traces, the system uses computer algorithms to process EEG data, compute features, and classify channels. This substitution maintains or improves localization reliability through consistent automated analysis while significantly reducing the complexity and labor involved in the process.
3Measurement precision
If intracranial electrodes are implanted for gold standard localization, then measurement precision is improved, but invasiveness and patient risk increase
Solution Approach 1:
The patent applies partial action by using a subset of available information (inter-ictal EEG data from routine monitoring) to achieve localization without requiring the full invasive procedure of intracranial electrode implantation. The system segments inter-ictal data into epochs and uses computational analysis to identify seizure-onset zones with sufficient precision for surgical planning in many cases, thereby avoiding the risks associated with invasive procedures while maintaining adequate measurement precision for clinical decision-making.
Data Source
AI summary
This specification discloses systems, methods, devices, and other techniques for determining the location of a seizure-generating region of the brain of a mammal.