Electrical status epilepticus during sleep electroencephalogram waveform identification and analysis based on a graph convolutional neural network

作者全名："Meng, Lu; Hu, Jinzhou; Deng, Yu; Hu, Yue"

作者地址："[Meng, Lu; Hu, Jinzhou] Northeastern Univ, Coll Informat Sci & Engn, Shenyang, Peoples R China; [Deng, Yu; Hu, Yue] Chongqing Med Univ, Chongqing, Peoples R China"

通信作者："Meng, L (通讯作者)，Northeastern Univ, Coll Informat Sci & Engn, Shenyang, Peoples R China."

来源：BIOMEDICAL SIGNAL PROCESSING AND CONTROL

ESI学科分类：ENGINEERING

WOS号：WOS:000799610000008

JCR分区：Q2

影响因子：5.1

年份：2022

卷号：77

期号：　

开始页：　

结束页：　

文献类型：Article

关键词：ESES; Epilepsy; Graph convolution; Graph convolution neural network; Automatic recognition; Pearson correlation

摘要："Electrical status epilepticus during sleep (ESES) is an epileptic syndrome in which neurons in the brain continue to discharge during the sleep phase and is common in mid-childhood. Affected patients often experience a decline in cognition, learning ability, memory, and expressive language skills. Therefore, timely and accurate diagnosis can effectively protect the health of a patient. Currently, the identification and analysis of ESES activities mainly rely on manual detection or traditional matching learning algorithms, such as morphology and template matching. These algorithms are time-consuming or have low accuracy. Therefore, in this paper, we propose a graph convolutional neural network that can automatically and accurately identify ESES activity from non-ESES activity. We divide the whole EEG signal into small segments, each of which covers one second of the EEG data. Then, we construct a graph according to each segment of the EEG data and train a graph convolutional neural network to classify the graph into two categories: ESES or non-ESES. Compared with other state-of-the-art algorithms, for the proposed algorithm, the accuracy, F1-Score, Area Under Curve(AUC) and sensitivity reaches 91.2%, 95.0%, 96.5%, and 91.3%, respectively, and outperforms the other algorithms."

基金机构：Northeastern University

基金资助正文：The authors declare the following financial interests/personal re-lationships which may be considered as potential competing interests: Lu Meng reports was provided by Northeastern University. Lu Meng reports a relationship with Northeastern University that includes: board membership. Lu Meng has patent pending to None. None