Epilepsy automatic detection method and system based on EEG

By combining the time-frequency analysis method of wavelet transform and short-time Fourier transform and deep learning model, the problem of insufficient real-time and accuracy in traditional epilepsy detection methods is solved, and accurate detection and prediction of epilepsy seizures are realized, and effective early warning and management are provided.

CN120477705APending Publication Date: 2025-08-15TIPMAX (SUZHOU) PHARM TECH CO LTD
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Patent Information

Application Number
CN202510623532.9
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2025-05-15
Publication Date
2025-08-15

AI Technical Summary

Technical Problem

The existing epilepsy detection methods rely on traditional neuroelectrophysiological diagnostic technology, which has strong subjectivity and poor real-time artificial analysis, making it difficult to predict quickly in the early stages of epilepsy seizures, and the existing technology lacks effective time-frequency feature fusion and deep learning, resulting in insufficient detection accuracy and real-time.

Method used

The time-frequency analysis method combined with wavelet transform and short-time Fourier transform is adopted, and combined with deep learning models, the accurate detection and prediction of epilepsy is achieved through multi-channel EEG signal acquisition, signal preprocessing, time-frequency feature extraction and multi-level feature fusion.

Benefits of technology

It improves the detection accuracy and real-time response capabilities of epilepsy seizures, and can provide effective early warning and management in complex environments to ensure timely intervention of epilepsy patients.

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Abstract

The invention discloses an EEG-based epilepsy automatic detection method and system, and belongs to the field of disease automatic detection. The method comprises the steps of collecting multi-channel electroencephalogram EEG signals in real time, conducting signal preprocessing, time-frequency feature extraction, deep learning analysis and multi-modal prediction, achieving accurate detection and prediction of epileptic seizure, generating real-time risk assessment of epileptic seizure through a prediction module in combination with multi-modal feature information, and achieving real-time risk assessment of epileptic seizure. The epilepsy early warning system sends an early warning signal to a user or a medical worker, starts corresponding intervention measures, has high detection precision and real-time response capability, and can provide effective early warning and management for epilepsy patients.
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