Epileptic seizure prediction method based on transition network data augmentation and fuzzy granular recurrence plot

Prediction of epileptic seizures is crucial for timely intervention and control. A significant challenge in this domain is the scarcity of preictal EEG data, which is important for accurate seizure prediction. To address this issue, a novel data augmentation method based on a transition network is proposed which not only enhances dataset diversity through a random walk algorithm but also preserves the spatial correlation between different EEG channels. Additionally, a noise-robust multivariate weighted fuzzy granular recurrence plot is introduced to extract nonlinear characteristics from EEG data, effectively mitigating the impact of noise on signal analysis. The multivariate weighted fuzzy granular recurrence plots are then input into the Inception V3 model for training the epilepsy prediction model. The novel method achieves state-of-the-art performance on the CHB-MIT database and American Epilepsy Society-Kaggle dataset. The key novelty of this work lies in the proposal of a transition network data augmentation method which overcomes the limitations of existing data augmentation techniques that often ignore inter-channel correlations or distort data distributions. Moreover, the introduction and development of fuzzy granular recurrence plot overcome the noise susceptibility of existing recurrence-plot-based EEG signal analysis methods and improves the extraction of detailed nonlinear features. By integrating these two novel methods into a unified framework, the performances of EEG data analysis and seizure prediction are effectively improved, offering a robust solution for clinical applications.