Denoising Sparse Autoencoder-Based Ictal PEG Classification

Automatic seizure detection technology can automatically mark the EEG by using the epileptic detection algorithm, which is helpful to the diagnosis and treatment of epileptic diseases. This paper presents an EEG classification framework based on the denoising sparse autoencoder. The denoising sparse autoencoder (DSAE) is an improved unsupervised deep neural network over sparse autoencoder and denoising autoencoder, which can learn the closest representation of the data. The sparsity constraint applied in the hidden layer of the network makes the expression of data as sparse as possible so as to obtain a more efficient representation of EEG signals. In addition, corrupting operation used in input data help to enhance the robustness of the system and make it suitable for the analysis of non-stationary epileptic EEG signals. In this paper, we first imported the pre-processed training data to the DSAE network and trained the network. A logistic regression classifier was connected to the top of the DSAE. Then, put the test data into the system for classification. Finally, the output results of the overall network were post-processed to obtain the final epilepsy detection results. In the two-class (nonseizure and seizure EEGs) problem, the system has achieved effective results with the average sensitivity of 100%, specificity of 100%, and recognition of 100%, showing that the proposed framework can be efficient for the classification of epileptic EEGs.