EEG Phase Synchronism Analysis for Seizure Prediction
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Solution Overview
Problem
Current cerebral imaging techniques, such as EEG, MEG, FMRI, and PET, cannot effectively map interactions between neuron networks, limiting the characterization of functional networks and the anticipation of epilepsy seizures, as they rely on linear analysis and fail to account for non-linear behavior and spatial interactions between distant brain regions.
Innovation Solution
A method for dynamic mapping of the brain using phase synchronism measurements in frequency bands between 0 and 2000 Hz, involving the creation of a database of synchronizations between electrophysiological signals from sensors, statistical validation, and detection of specific synchronization patterns to anticipate seizures or other physiological/pathological states.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional linear analysis methods are used for cerebral imaging, then the analysis process is simple, but the ability to map interactions between neuron networks and anticipate seizures is insufficient
Solution Approach 1:
The patent transforms EEG analysis from traditional linear frequency domain methods to nonlinear time-frequency domain analysis. By calculating instantaneous frequency and phase synchrony parameters dynamically, the system captures non-stationary cerebral signal characteristics, enabling precise seizure anticipation while maintaining computational feasibility through parameter transformation rather than system complexity increase
Solution Approach 2:
The invention introduces dynamic analysis of phase synchrony between cerebral signals. By continuously tracking time-varying phase relationships and synchrony indices, the system adapts to changing brain states, allowing real-time seizure prediction that responds to dynamic neural interactions rather than relying on static linear correlations
2Loss of information
If phase synchronism measurements are used to map cerebral interactions, then the characterization of functional networks is improved, but the computational complexity increases
Solution Approach 1:
The patent extracts specific informative parameters (instantaneous phase, frequency, and synchrony indices) from complex cerebral signals. By isolating these key features that directly reflect neural interactions, the system reduces the dimensionality of processed data while preserving essential information about functional network dynamics, balancing information retention with computational efficiency
3Reliability
If real time analysis of cerebral electromagnetic activity is performed, then seizure anticipation capability is improved, but the processing time and computational load increase
Solution Approach 1:
The patent divides continuous cerebral signals into discrete time windows for analysis. By segmenting the signal and computing synchrony parameters within each window independently, the system achieves real-time processing through batch computation of segmented data, balancing temporal resolution with computational efficiency and maintaining reliable seizure prediction
Data Source
AI summary
A method for analyzing synchronizations of the electroencephalography of an individual using a set of sensors starting from cerebral electromagnetic analysis of the individual. The method creates a database by acquisition and digitization of electrophysiological signals output from the sensors, and calculates the degree of synchronization existing between all pairs of sensors recorded in an assembly protocol, in frequency bands between 0 and 2000 Hz, to build up the database of classes each characterizing a reference state. The method further performs statistical validation of a period analyzed in real time, which assigns this period to a class in the database, and detects a specific period with a determined degree of synchronization. A device implements this method.