Double Discrete Variational Autoencoder for Epileptic EEG Signals Classification

Abstract

Electroencephalography (EEG) plays a key role in the clinical evaluation of epilepsy and provides strong support for treatment decisions. However, analyzing and decoding EEG recordings is a burdensome task for neurologists and clinical experts. Existing automated detection techniques make considerable efforts in feature engineering, but often fall short when it comes to representing complex patterns in EEG signals. Deep learning methods allow for higher-order representations and intricate pattern learning, experiencing explosive success in the performance of automated EEG diagnostics. In this paper, we propose a novel Double Discrete Variational AutoEncoder (D2-VAE) network to learn the latent representations in EEG signals and perform deep discretization. Specifically, the method builds a learnable codebook based on the Vector Quantized Variational AutoEncoder (VQ-VAE) to obtain a generic representation of the EEG signal. The discretization of the local patterns of the signal is obtained by codebook queries, whereas the discretization of the global information of the signal is characterized by building a histogram of the quantization patterns. Such local-global signal portrayal is more attuned to the single-mode repetition and multi-mode mixing that characterizes epileptic signals. Multiple epilepsy diagnostic tasks and multiple evaluation metrics are used to validate the effectiveness of the proposed method for the classification of complex EEG signals. The experimental results show that D2-VAE possesses a low-dimensional yet powerful quantitative representation, with a significant improvement in performance over existing methods.

Keywords

• Brain signals
• epilepsy
• deep learning
• discrete representation
• EEG