Epilepsy Seizure classification based on ConvGru-CNN Network

Electroencephalogram (EEG) is a physiological signal used to monitor the electrical activity within the cortical monitoring area of the patient’s brain. It is commonly used for real-time detection of seizure states and to determine seizure types. Traditional manual detection methods are not only time-consuming but also subjective. Therefore, using deep learning methods to automatically classify various types of epileptic signals can not only reduce the long-term monitoring work of neuroscientists on patients’ EEG signals, but also improve the accuracy of patient diagnosis to a certain extent. In this study, we first used transverse central parietal (TCP) montage to emphasize the spike activity and reduce the influence of noise on the publicly available Temple University Hospital (TUH) dataset, resulting in 20 channels of montage-processed data information. Then, we sampled each epileptic seizure data 34 times on all channels using a 1-second time window and 0.25-second time step, and extracted the amplitude of 1-12Hz from each channel using fast Fourier transform. Combined with the proposed model, experiments were conducted for training and validation, achieving an accuracy of 88.38% in the classification of seven different types of epileptic seizures, which outperformed some existing traditional methods. This indicates that the proposed method can be effectively used in the task of classifying epileptic seizures.