论文标题
SSGCNET：用于癫痫脑电图分类的稀疏光谱图卷积网络
SSGCNet: A Sparse Spectra Graph Convolutional Network for Epileptic EEG Signal Classification
论文作者
论文摘要
在本文中，我们提出了一个稀疏的光谱图卷积网络（SSGCNET），用于解决癫痫发作的脑电图信号分类问题。目的是实现轻巧的深度学习模型，而不会丢失模型分类精度。我们提出了一个加权邻里场图（WNFG）来表示EEG信号，从而降低了图节点之间的冗余边缘。 WNFG比常规解决方案具有较低的时间复杂性和记忆使用情况。使用图表表示，顺序图卷积网络基于稀疏权重修剪技术和乘数的交替方向方法（ADMM）的组合。我们的方法可以降低计算复杂性而不会影响分类精度。我们还为提出的方法提供了收敛结果。该方法的性能在公共和临床实现数据集中说明。与现有文献相比，我们的脑电图信号的WNFG达到了冗余降低的10倍，而我们的方法可实现多达97倍的模型修剪，而不会损失分类精度。
In this article, we propose a sparse spectra graph convolutional network (SSGCNet) for solving Epileptic EEG signal classification problems. The aim is to achieve a lightweight deep learning model without losing model classification accuracy. We propose a weighted neighborhood field graph (WNFG) to represent EEG signals, which reduces the redundant edges between graph nodes. WNFG has lower time complexity and memory usage than the conventional solutions. Using the graph representation, the sequential graph convolutional network is based on a combination of sparse weight pruning technique and the alternating direction method of multipliers (ADMM). Our approach can reduce computation complexity without effect on classification accuracy. We also present convergence results for the proposed approach. The performance of the approach is illustrated in public and clinical-real datasets. Compared with the existing literature, our WNFG of EEG signals achieves up to 10 times of redundant edge reduction, and our approach achieves up to 97 times of model pruning without loss of classification accuracy.
