Neurological Signal Monitoring System for Seizure Prediction
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Solution Overview
Problem
Current methods for predicting epileptic seizures from neurological electrical signals, such as EEG and ECoG, face challenges in accurately identifying the onset of seizures in real-time, particularly in distinguishing between normal and seizure states using pattern analysis and feature extraction techniques.
Innovation Solution
A neurological signal monitoring system that uses a combination of signal processing techniques, including wavelet decomposition and pattern analysis, to identify anomalies in brain activity patterns, which are then used to predict seizure onset by comparing pattern counts and ratios against historical thresholds, with the ability to provide real-time alerts and potentially stimulate the brain to prevent seizures.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Difficulty of detecting and measuring
If pattern analysis and feature extraction techniques are used to identify seizure onset from neurological electrical signals, then seizure detection capability is improved, but accuracy in distinguishing between normal and seizure states deteriorates
Solution Approach 1:
The patent segments the neurological signal analysis into multiple distinct feature extraction categories (spectral features, temporal features, spatial features, and non-linear features). Each feature type captures different aspects of seizure activity, allowing the system to analyze multiple dimensions of the signal simultaneously. This segmentation enables more precise differentiation between normal and seizure states by combining information from various feature domains.
Solution Approach 2:
The patent employs multiple parameter representations of the same signal, including frequency domain parameters (power spectral density, band power ratios), time domain parameters (mean, variance, skewness), and derived parameters (feature combinations, normalized values). By transforming the signal into multiple parameter spaces and analyzing changes across these parameters, the system achieves more accurate seizure state distinction.
2Loss of time
If real-time analysis of neurological signals is performed to predict seizure onset, then timely seizure prediction is improved, but system complexity deteriorates
Solution Approach 1:
The patent implements preliminary action by continuously extracting and analyzing multiple feature types in real-time, maintaining a running assessment of seizure risk. The system pre-computes various feature statistics and maintains feature vectors that are ready for immediate seizure prediction decisions. This continuous preliminary analysis enables timely seizure prediction without requiring complex post-processing.
Solution Approach 2:
The patent creates a multi-functional feature extraction system that simultaneously computes spectral features, temporal features, spatial features, and non-linear features from the same input signal. This universal feature extraction framework handles multiple analysis tasks (seizure detection, prediction, and characterization) using a unified approach, reducing overall system complexity while maintaining comprehensive real-time monitoring capabilities.
Data Source
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AI summary
A monitoring or predicting system to detect the onset of a neurological episode, the system comprising :a neurological electrical input, the input being a digital representation of a neurologically derived signal; a converter to convert the digital signal into a digital data string; a pattern analyser to identify recurring patterns in the digital data string; and a monitor to measure a pattern-derived parameter, wherein an output from the monitor gives an indication of the onset or occasion of a neuronal activity in dependence on the pattern-derived parameter.