GRP-DNet: A gray recurrence plot-based densely connected convolutional network for classification of epileptiform EEG

计算机科学 脑电图 人工智能 模式识别(心理学) 滑动窗口协议 癫痫 卷积神经网络 窗口(计算) 神经科学 心理学 操作系统
作者
Ming Zeng,Xiaonei Zhang,Chunyu Zhao,Xiangzhe Lu,Qing-Hao Meng
出处
期刊:Journal of Neuroscience Methods [Elsevier BV]
卷期号:347: 108953-108953 被引量:27
标识
DOI:10.1016/j.jneumeth.2020.108953
摘要

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. 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. 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. Our GRP-DNet system was superior to the existing competitive classification systems using the same database. The GRP-DNet is a potentially powerful system for identifying and classifying EEG signals recorded from different brain states.
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