A novel wavelet-based model for EEG epileptic seizure detection using multi-context learning

Epileptic seizure detection has gained increasing attention in clinical therapy. Scalp electroencephalogram (EEG) analysis is a common way to capture brain abnormality for seizure onset detection. This paper presents a novel context-learning based approach using multi-feature fusion to compensate for incomplete description of single feature in epileptic EEG signals. First, EEG scalogram sequence is generated using wavelet transform to represent the time-frequency information. Second, three sets of EEG context features are unsupervisedly learned in parallel by using global principal component analysis (GPCA), stacked denoising autoencoders (SDAEs) and EEG embeddings, respectively. Finally, the multi-features are concatenated into a fixed-length feature vector for seizure classification. The experimental results conducted on two real EEG datasets demonstrate that the proposed cross-patient learning model is able to extract meaningful context features from different perspectives, and hence can detect the onset of epileptic seizure effectively.