.png){kind=icon}
返回
Spatial Enhanced Pattern Through Graph Convolutional Neural Network for Epileptic EEG Identification
.png){kind=icon}
PRE
AI
DOI：10.1142/S0129065722500332.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Feature extraction is an essential procedure in the detection and recognition of epilepsy, especially for clinical applications. As a type of multichannel signal, the association between all of the channels in EEG samples can be further utilized. To implement the classification of epileptic seizures from the nonseizures in EEG samples, one graph convolutional neural network (GCNN)-based framework is proposed for capturing the spatial enhanced pattern of multichannel signals to characterize the behavior of EEG activity, which is capable of visualizing the salient regions in each sequence of EEG samples. Meanwhile, the presented GCNN could be exploited to discriminate normal, ictal and interictal EEGs as a novel classifier. To evaluate the proposed approach, comparison experiments were conducted between state-of-the-art techniques and ours. From the experimental results, we found that for ictal and interictal EEG signal discrimination, the presented approach can achieve a sensitivity of 98.33%, specificity of 99.19% and accuracy of 98.38%.
Keyword:
Epileptic seizure
electroencephalogram
classification
feature extraction
期刊
IF:
6.4
论文数:
1.2K
被引数:
3.3K
机构
引用论文
Graph Attention Network with Focal Loss for Seizure Detection on Electroencephalography Signals
PREAI
分享收藏
Absence of the LiTat 1.3 (CATT antigen) gene in Trypanosoma brucei gambiense stocks from Cameroon喀麦隆的布氏冈比亚锥虫种群中缺乏LiTat 1.3 (CATT抗原) 基因.png)
Acta Tropica
Acta TropicaIF0
PREAI
分享收藏
查看更多内容