EEG Signal Processing for Irregular Phase Detection
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Solution Overview
Problem
Conventional algorithms struggle to accurately detect the irregular phase of epilepsy onset in EEG signals due to their non-stationary characteristics, which are challenging to interpret because of complex interactions between excitatory and suppressor cells.
Innovation Solution
A method that combines spike detection, instantaneous frequency oscillation energy analysis, and complexity analysis to identify specific neural waveforms in EEG signals, using empirical mode decomposition and calculating energy and complexity changes within defined intervals to determine the irregular phase.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional algorithms are used to detect epilepsy phases in EEG signals, then the detection process is simple, but the accuracy of detecting irregular phase is low due to non-stationary characteristics
Solution Approach 1:
The patent segments the EEG signal analysis into three distinct components: spike detection (counting neural discharges), energy analysis (calculating oscillation energy in different frequency bands), and complexity analysis (computing sample entropy). Each segment addresses specific characteristics of the irregular phase, and their combined results improve detection accuracy while keeping each individual analysis module manageable in complexity
Solution Approach 2:
The patent transforms the EEG signal from time domain to frequency domain through spectral analysis, enabling the calculation of energy distribution across different frequency bands. This parameter transformation allows the system to capture non-stationary characteristics that conventional time-domain algorithms miss, directly improving detection accuracy for irregular phases
2Measurement precision
If multiple analysis methods are combined to improve detection accuracy, then the detection precision improves, but the computational complexity increases
Solution Approach 1:
The computational workload is divided into three separate analysis streams (spike detection, energy analysis, complexity analysis) that can be processed independently and then integrated. This segmentation allows for optimized computation of each component without requiring excessive computational power for the overall system
Solution Approach 2:
The patent applies analysis at multiple scales and levels: spike detection operates on raw signal peaks, energy analysis operates on frequency-banded segments, and complexity analysis operates on reconstructed phases. This multi-level partial analysis ensures adequate detection accuracy without performing exhaustive analysis on the entire signal at maximum computational cost
Data Source
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AI summary
A method and a system for processing an electroencephalogram (EEG) signal are provided. The method for processing the EEG signal includes: performing a spike detection on the EEG signal to obtain a spike distribution waveform, performing an instantaneous frequency oscillation energy analysis on the EEG signal to obtain multiple energy distribution waveforms; performing a complexity analysis on the EEG signal to obtain a complexity change waveform, obtaining a determination result of a specified neural waveform based on the spike distribution waveform, the energy distribution waveforms, and the complexity change waveform, and outputting the determination result.