D2PAM: Epileptic seizures prediction using adversarial deep dual patch attention mechanism

Abstract

Epilepsy is considered as a serious brain disorder in which patients frequently experience seizures. The seizures are defined as the unexpected electrical changes in brain neural activity, which leads to unconsciousness. Existing researches made an intense effort for predicting the epileptic seizures using brain signal data. However, they faced difficulty in obtaining the patients' characteristics because the model's distribution turned to fake predictions, affecting the model's reliability. In addition, the existing prediction models have severe issues, such as overfitting and false positive rates. To overcome these existing issues, we propose a deep learning approach known as Deep dual-patch attention mechanism (D²PAM) for classifying the pre-ictal signals of people with Epilepsy based on the brain signals. Deep neural network is integrated with D²PAM, and it lowers the effect of differences between patients to predict ES. The multi-network design enhances the trained model's generalisability and stability efficiently. Also, the proposed model for processing the brain signal is designed to transform the signals into data blocks, which is appropriate for pre-ictal classification. The earlier warning of epilepsy with the proposed model obtains the auxiliary diagnosis. The data of real patients for the experiments provides the improved accuracy by D²PAM approximation compared to the existing techniques. To be more distinctive, the authors have analysed the performance of their work with five patients, and the accuracy comes out to be 95%, 97%, 99%, 99%, and 99% respectively. Overall, the numerical results unveil that the proposed work outperforms the existing models.