A multi-frame network model based on time-frequency analysis for epilepsy prediction

In this paper, a multi-frame network model based on time-frequency analysis is proposed for epilepsy prediction, which is used to electroencephalogram (EEG) data classification. Firstly, short-term Fourier transform and wavelet packet transform is used to extract time-frequency information respectiv...

Full description

Saved in:
Bibliographic Details
Published inChinese Control Conference pp. 8262 - 8267
Main Authors Zhang, Feng, Wei, Xile, Lu, Liangfu
Format Conference Proceeding
LanguageEnglish
Published Technical Committee on Control Theory, Chinese Association of Automation 28.07.2024
Subjects
Online AccessGet full text
ISSN1934-1768
DOI10.23919/CCC63176.2024.10662334

Cover

Loading…
More Information
Summary:In this paper, a multi-frame network model based on time-frequency analysis is proposed for epilepsy prediction, which is used to electroencephalogram (EEG) data classification. Firstly, short-term Fourier transform and wavelet packet transform is used to extract time-frequency information respectively, which can obtain two data representations. Secondly, convolutional neural network and long and short-term memory network are constructed to extract high-level features from above two data representations. An innovative module named MFB++ is then used for feature fusion. Finally, the fused feature is input into classifier to obtain classification result. The experimental results show that the proposed model achieves sensitivity of 98.25% and 94.79%, and false prediction alarm rate of 0.07/h and 0.00/h on CHB-MIT and TJU-HH datasets, respectively. Moreover, the significance test shows that our model performs significantly better than unspecific random predictor for all subjects. The proposed model achieves expected prediction performance, which has important practical significance for improving the quality of life of epilepsy patients.
ISSN:1934-1768
DOI:10.23919/CCC63176.2024.10662334