Intracranial electroencephalogram epileptic seizure early warning method based on multipath map brain network modeling

By constructing a multi-channel brain map and learning its mode, the problem of failure to fully consider the complexity and frequency information of brain networks in the existing technology is solved, and more efficient epileptic seizure prediction and early warning are achieved.

CN119856911AActive Publication Date: 2025-04-22INST OF AUTOMATION CHINESE ACAD OF SCI
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Patent Information

Application Number
CN202510357007.7
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2025-03-25
Publication Date
2025-04-22
Estimated Expiration
2045-03-25

AI Technical Summary

Technical Problem

The prior art fails to fully consider the complexity of the brain network when building a brain network model, ignores the rich frequency information in multiple relationships and iEEG signals, resulting in insufficient performance in epilepsy prediction.

Method used

Using a multi-pathogram brain network modeling method, a multi-pathogram map is constructed through a graph encoder and a joint fusion module, including amplitude-amplitude coupled sub-graph, phase-phase coupled sub-graph and phase-amplitude coupled sub-graph, to learn the patterns in the multi-pathogram and perform feature fusion to improve the accuracy of epilepsy prediction.

Benefits of technology

Effectively representing multiple relationships in the brain network and making full use of the frequency information in the iEEG signal, improving the accuracy of epilepsy prediction and enhancing the reliability of early warning.

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Abstract

The invention discloses an intracranial electroencephalogram epileptic seizure early warning method based on multipath map brain network modeling, and belongs to the technical field of electroencephalogram analysis. The method comprises the following steps: providing a multi-path map brain network modeling framework for epileptic seizure prediction; the multi-channel brain map is constructed based on three cross-frequency coupling (CFC) relationships in neuroscience, a frequency band in each channel of the electroencephalogram signal forms nodes of the map, and the three CFC relationships form edges of the map; and designing a multi-path brain map learning network with a joint fusion module to learn modes in the multi-path brain map to form a final representation. According to the method, the iEEG signals can be analyzed, the modes in the iEEG signals can be mined by utilizing the designed multi-channel brain map learning network, uniform characterization is formed, and the accuracy of epileptic seizure prediction is improved.
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