Jiang Lurong, Fan Qikai, Ren Juntao, Dong Fang, Jiang Tiejia, Liu Junbiao
School of Information Science and Engineering, Zhejiang Sci-Tech University, Hangzhou, China.
College of Information and Electric Engineering, Zhejiang University City College, Hangzhou, China.
Front Neurosci. 2023 Mar 16;17:1150668. doi: 10.3389/fnins.2023.1150668. eCollection 2023.
Children with benign childhood epilepsy with centro-temporal spikes (BECT) have spikes, sharps, and composite waves on their electroencephalogram (EEG). It is necessary to detect spikes to diagnose BECT clinically. The template matching method can identify spikes effectively. However, due to the individual specificity, finding representative templates to detect spikes in actual applications is often challenging.
This paper proposes a spike detection method using functional brain networks based on phase locking value (FBN-PLV) and deep learning.
To obtain high detection effect, this method uses a specific template matching method and the 'peak-to-peak' phenomenon of montages to obtain a set of candidate spikes. With the set of candidate spikes, functional brain networks (FBN) are constructed based on phase locking value (PLV) to extract the features of the network structure during spike discharge with phase synchronization. Finally, the time domain features of the candidate spikes and the structural features of the FBN-PLV are input into the artificial neural network (ANN) to identify the spikes.
Based on FBN-PLV and ANN, the EEG data sets of four BECT cases from the Children's Hospital, Zhejiang University School of Medicine are tested with the AC of 97.6%, SE of 98.3%, and SP 96.8%.
伴有中央颞区棘波的儿童良性癫痫（BECT）患儿的脑电图（EEG）上有棘波、锐波和复合波。临床上诊断BECT需要检测到棘波。模板匹配方法可以有效地识别棘波。然而，由于个体特异性，在实际应用中找到具有代表性的模板来检测棘波往往具有挑战性。
本文提出一种基于锁相值的功能脑网络（FBN-PLV）和深度学习的棘波检测方法。
为了获得高检测效果，该方法使用特定的模板匹配方法和导联的“峰峰值”现象来获得一组候选棘波。利用这组候选棘波，基于锁相值（PLV）构建功能脑网络（FBN），以提取棘波放电期间具有相位同步的网络结构特征。最后，将候选棘波的时域特征和FBN-PLV的结构特征输入人工神经网络（ANN）以识别棘波。
基于FBN-PLV和ANN，对浙江大学医学院附属儿童医院的4例BECT病例的EEG数据集进行测试，准确率为97.6%，灵敏度为98.3%，特异度为96.8%。
