SSGCNet: A Sparse Spectra Graph Convolutional Network for Epileptic EEG Signal Classification

In this article, we propose a sparse spectra graph convolutional network (SSGCNet) for epileptic electroencephalogram (EEG) signal classification. The goal is to develop a lightweighted deep learning model while retaining a high level of classification accuracy. To do so, we propose a weighted neighborhood field graph (WNFG) to represent EEG signals. The WNFG reduces redundant edges between graph nodes and has lower graph generation time and memory usage than the baseline solution. The sequential graph convolutional network is further developed from a WNFG by combining sparse weight pruning and the alternating direction method of multipliers (ADMM). Compared with the state-of-the-art method, our method has the same classification accuracy on the Bonn public dataset and the spikes and slow waves (SSW) clinical real dataset when the connection rate is ten times smaller.

Automated summary

In this article, a sparse spectra graph convolutional network (SSGCNet) is proposed for epileptic electroencephalogram (EEG) signal classification. The aim is to develop a lightweight deep learning model that maintains a high level of classification accuracy. To achieve this, a weighted neighborhood field graph (WNFG) is introduced to represent EEG signals. The WNFG reduces redundant edges and has lower graph generation time and memory usage. The sequential graph convolutional network is then extended from the WNFG using sparse weight pruning and the alternating direction method of multipliers (ADMM). Compared to the state-of-the-art method, our approach achieves the same classification accuracy with a ten times smaller connection rate on the Bonn public dataset and the spikes and slow waves (SSW) clinical real dataset.