Key result
The proposed SSGCNet model maintained high classification accuracy for epileptic EEG signals while achieving a ten-fold compression rate compared to state-of-the-art methods.
Why the study?
The goal was to develop a lightweight deep learning model for epileptic EEG signal classification that retains high classification accuracy.
SSGCNet provides a lightweight, highly compressed deep learning model for epileptic EEG classification that maintains high accuracy while significantly reducing computational cost and memory usage.
May support efficient EEG epilepsy classification; leaves open prospective clinical validation before practice change.
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.
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Wang et al. (2023) studied Epilepsy (n=20). Sparse Spectra Graph Convolutional Network (SSGCNet) vs. Traditional machine learning and baseline deep learning models (MLP, GNN, 1-D CNN) was evaluated on Classification accuracy and model compression rate. The proposed SSGCNet model maintained high classification accuracy for epileptic EEG signals while achieving a ten-fold compression rate compared to state-of-the-art methods.
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