Background

The classification of epileptiform electroencephalogram (EEG) signals has been treated as an important but challenging issue for realizing epileptic seizure detection. In this work, combing gray recurrence plot (GRP) and densely connected convolutional network (DenseNet), we developed a novel classification system named GRP-DNet to identify seizures and epilepsy from single-channel, long-term EEG signals.

New methods

The proposed GRP-DNet classification system includes three main modules: 1) input module takes an input long-term EEG signal and divides it into multiple short segments using a fixed-size non-overlapping sliding window (FNSW); 2) conversion module transforms short segments into GRPs and passes them to the DenseNet; 3) fusion and decision, the predicted label of each GRP is fused using a majority voting strategy to make the final decision.

Results

Six different classification experiments were designed based on a publicly available benchmark database to evaluate the effectiveness of our system. Experimental results showed that the proposed GRP-DNet achieved an excellent classification accuracy of 100 % in each classification experiment, Furthermore, GRP-DNet gave excellent computational efficiency, which indicates its tremendous potential for developing an EEG-based online epilepsy diagnosis system.

Comparison with existing methods

Our GRP-DNet system was superior to the existing competitive classification systems using the same database.

Conclusions

The GRP-DNet is a potentially powerful system for identifying and classifying EEG signals recorded from different brain states.

中文翻译：

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GRP-DNet：基于灰色递归图的密集连接卷积网络，用于癫痫样脑电图的分类

背景

癫痫样脑电图（EEG）信号的分类已被视为实现癫痫发作检测的重要但具有挑战性的问题。在这项工作中，结合了灰色递归图（GRP）和紧密连接的卷积网络（DenseNet），我们开发了一种名为GRP-DNet的新型分类系统，以从单通道长期脑电信号中识别癫痫和癫痫病。

新方法

提出的GRP-DNet分类系统包括三个主要模块：1）输入模块获取输入的长期EEG信号，并使用固定大小的不重叠滑动窗口（FNSW）将其分为多个短段；2）转换模块将短片段转换为GRP，并将其传递给DenseNet；3）融合和决策，使用多数表决策略融合每个GRP的预测标签，以做出最终决策。

结果

根据公开的基准数据库设计了六个不同的分类实验，以评估我们系统的有效性。实验结果表明，提出的GRP-DNet在每个分类实验中均达到了100％的优良分类精度，此外，GRP-DNet具有出色的计算效率，表明其在开发基于EEG的在线癫痫诊断系统方面的巨大潜力。

与现有方法的比较

我们的GRP-DNet系统优于使用相同数据库的现有竞争分类系统。

结论

GRP-DNet是用于识别和分类从不同大脑状态记录的EEG信号的潜在强大系统。