EEG-Based Seizure Prediction Via GhostNet and Imbalanced Learning

Seizure prediction task based on electroencephalogram (EEG) is a typical imbalanced classification problem since the preictal samples are much less than the interictal samples. Resampling is a common method to solve class-imbalance problems in the area of seizure prediction. However, up-sampling has...

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Veröffentlicht in:	IEEE sensors letters 2023-12, Vol.7 (12), p.1-4
Hauptverfasser:	Mao, Tingting, Li, Chang, Zhao, Yuchang, Song, Rencheng, Chen, Xun
Format:	Artikel
Sprache:	eng
Schlagworte:	Algorithms Brain modeling class rebalanced loss (CRB-loss) function class-imbalance problem Convolution Convolutional neural networks Electroencephalography end-to-end Epilepsy Feature extraction Feature maps ghost network (GhostNet) Learning Predictive models Resampling Sampling seizure prediction Sensor networks
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