Noise-Aware Epileptic Seizure Prediction Network via Self-Attention Feature Alignment

Recently, deep neural networks have been extensively used to extract features from EEG data for epileptic seizure prediction in the epilepsy diagnosis community. Many existing works in literature either use the ultimate-layer feature or aggregate multi-layer features via straightforward concatenation or element-wise addition, but they do not pay a special attention to the contextual consistency between these features as well as the involved noise in these features. To address the above problem, we propose a Noise-aware epileptic seizure prediction network via Self-attention Feature Alignment, called NSFA-Net. The NSFA-Net consists of two modules: a self-attention backbone module to extract multi-layer features from the input EEG data, and a time-frequency feature alignment module to align these features for maintaining the contextual consistency. In addition, during the training process, a noise-aware regularizer is introduced to alleviate the negative influence of noise that is generally inevitable in EEG data. The average sensitivities of the proposed method on the CHB-MIT and Kaggle datasets are 98.68% and 93.57% respectively, and the average false prediction rates are 0.038/h and 0.060/h respectively. These experimental results show the superiority of the proposed method to some state-of-the-art methods.