PDF(Pubmed)
Abstract：
UNASSIGNED: 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.
UNASSIGNED: This paper proposes a spike detection method using functional brain networks based on phase locking value (FBN-PLV) and deep learning.
UNASSIGNED: 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.
UNASSIGNED: 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%.
UNASSIGNED: This paper proposes a spike detection method using functional brain networks based on phase locking value (FBN-PLV) and deep learning.
UNASSIGNED: 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.
UNASSIGNED: 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%.
摘要：
■患有良性儿童癫痫伴中央颞部尖峰（BECT）的儿童有尖峰，夏普,和他们的脑电图（EEG）上的复合波。有必要检测尖峰以临床诊断BECT。模板匹配方法可以有效地识别尖峰。然而,由于个体的特异性，在实际应用中找到有代表性的模板来检测尖峰通常是具有挑战性的。
■本文提出了一种基于锁相值（FBN-PLV）和深度学习的使用功能脑网络的尖峰检测方法。
■要获得高检测效果，该方法使用特定的模板匹配方法和蒙太奇的“峰峰现象”来获得一组候选尖峰。有了一组候选尖峰，基于相位锁定值（PLV）构建功能脑网络（FBN），以提取具有相位同步的尖峰放电过程中的网络结构特征。最后,将候选尖峰的时域特征和FBN-PLV的结构特征输入到人工神经网络(ANN)中以识别尖峰。
■基于FBN-PLV和ANN，儿童医院4例BECT病例的脑电图数据集，浙江大学医学院的AC检测为97.6%，SE为98.3%，SP96.8%。
■本文提出了一种基于锁相值（FBN-PLV）和深度学习的使用功能脑网络的尖峰检测方法。
■要获得高检测效果，该方法使用特定的模板匹配方法和蒙太奇的“峰峰现象”来获得一组候选尖峰。有了一组候选尖峰，基于相位锁定值（PLV）构建功能脑网络（FBN），以提取具有相位同步的尖峰放电过程中的网络结构特征。最后,将候选尖峰的时域特征和FBN-PLV的结构特征输入到人工神经网络(ANN)中以识别尖峰。
■基于FBN-PLV和ANN，儿童医院4例BECT病例的脑电图数据集，浙江大学医学院的AC检测为97.6%，SE为98.3%，SP96.8%。
