Seizure Detection via Parameter Space Path Evolution
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Solution Overview
Problem
Current automatic seizure detection algorithms in EEG recordings are limited by subjectivity and slowness in visual analysis, and they often result in false detections, especially in ICU settings where non-convulsive seizures are common and require immediate attention, due to their reliance on signal characteristics like power and periodicity without considering the time evolution of seizure patterns.
Innovation Solution
The method quantifies the time evolution of brain wave signals by forming paths in a parameter space defined by signal parameters, using path length or derivatives as evolution indicators to detect seizure activity, which can be used to improve the accuracy of seizure detection and reduce false positives.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If automatic seizure detection algorithms rely on signal characteristics like power and periodicity, then detection speed is improved, but measurement precision deteriorates due to false detections
Solution Approach 1:
The patent transitions from analyzing single signal characteristics (power, periodicity) to examining the temporal evolution trajectory of multiple signal parameters in a multi-dimensional parameter space. This dimensional expansion allows the system to capture the dynamic progression of seizure patterns over time, distinguishing true seizures from false positives by analyzing how parameters evolve sequentially rather than relying on static thresholds.
Solution Approach 2:
The system pre-defines characteristic evolution paths and patterns in the parameter space that represent typical seizure trajectories. By having these reference patterns established beforehand, the detection algorithm can quickly compare incoming signal evolution against known seizure patterns, maintaining high detection speed while improving accuracy through pattern matching rather than simple threshold comparison.
2Measurement precision
If visual analysis is used to review long-term EEG recordings, then measurement precision is improved through expert pattern recognition, but loss of time increases due to several hours of review required
Solution Approach 1:
The patent replaces the mechanical process of manual visual analysis with an automated computational system that processes EEG signals through mathematical transformations. The system automatically extracts features, constructs evolution paths in parameter space, and detects seizures using algorithmic pattern recognition, eliminating the time-consuming manual review process while maintaining detection accuracy through sophisticated signal processing.
Solution Approach 2:
The invention introduces an intermediary computational layer between raw EEG signals and seizure detection. This intermediary process automatically performs feature extraction, parameter space transformation, and evolution analysis, serving as a bridge that translates complex EEG data into actionable seizure detections without requiring direct human visual inspection, thus dramatically reducing analysis time.
3Measurement precision
If semi-automatic detectors require human observers to mark one seizure instance, then measurement precision is improved for patient-specific detection, but ease of operation deteriorates due to manual intervention required
Solution Approach 1:
The system performs automatic patient-specific adaptation without requiring manual seizure marking. The algorithm independently analyzes the patient's EEG signals, learns their specific seizure patterns through unsupervised or self-supervised learning from the signal evolution trajectories, and automatically adjusts detection parameters. This self-service capability eliminates the need for manual intervention while maintaining high detection accuracy tailored to each patient's unique characteristics.
4Ease of manufacture
If automatic detectors use simple signal characteristics for detection, then ease of manufacture is improved, but reliability deteriorates due to false detections in ICU settings
Solution Approach 1:
The patent addresses the reliability issue by adding the temporal evolution dimension to the analysis. Instead of relying solely on simple signal characteristics that prone to false detections, the system examines how multiple parameters evolve over time, constructing trajectories in parameter space. This additional temporal dimension provides contextual information that distinguishes true seizures from artifacts, significantly improving reliability while building upon relatively simple signal processing foundations.
Data Source
AI summary
Method, apparatus and computer program product for monitoring seizure activity in brain are disclosed. At least one parameter set sequence is derived from brain wave signal data obtained from a subject, wherein each parameter set sequence comprises sequential parameter sets and each parameter set comprises values for at least two signal parameters, the values being derived from the brain wave signal data. A path formed by each of the at least one parameter set sequence in a parameter space is determined, thereby to obtain at least one path. The parameter space is defined by the at least two signal parameters. At least one evolution indicator is calculated, each evolution indicator quantifying the evolution occurred in respective path formed in a given time period in the parameter space. The at least one evolution indicator is then employed to produce an indication of seizure activity in the brain wave signal data.