A novel multi-view deep learning model using STFT and SSDA effectively detects epileptic seizures from multi-channel EEG signals.
With the advances in pervasive sensor technologies, physiological signals can be captured continuously to prevent the serious outcomes caused by epilepsy. Detection of epileptic seizure onset on collected multi-channel electroencephalogram (EEG) has attracted lots of attention recently. Deep learning is a promising method to analyze large-scale unlabeled data. In this paper, we propose a multi-view deep learning model to capture brain abnormality from multi-channel epileptic EEG signals for seizure detection. Specifically, we first generate EEG spectrograms using short-time Fourier transform (STFT) to represent the time-frequency information after signal segmentation. Second, we adopt stacked sparse denoising autoencoders (SSDA) to unsupervisedly learn multiple features by considering both intra and inter correlation of EEG channels, denoted as intra-channel and cross-channel features, respectively. Third, we add an SSDA-based channel selection procedure using proposed response rate to reduce the dimension of intra-channel feature. Finally, we concatenate the learned multi-features and apply a fully-connected SSDA model with softmax classifier to jointly learn the cross-patient seizure detector in a supervised fashion. To evaluate the performance of the proposed model, we carry out experiments on a real world benchmark EEG dataset and compare it with six baselines. Extensive experimental results demonstrate that the proposed learning model is able to extract latent features with meaningful interpretation, and hence is effective in detecting epileptic seizure.
Yuan et al. (Sun,) studied this question.