Li Qiong, Gao Jianbo, Zhang Ziwen, Huang Qi, Wu Yuan, Xu Bo
School of Computer, Electronics and Information, Guangxi University, Nanning, China.
Center for Geodata and Analysis, Faculty of Geographical Science, Beijing Normal University, Beijing, China.
Front Physiol. 2020 Aug 5;11:828. doi: 10.3389/fphys.2020.00828. eCollection 2020.
Epilepsy is one of the most common disorders of the brain. Clinically, to corroborate an epileptic seizure-like symptom and to find the seizure localization, electroencephalogram (EEG) data are often visually examined by a clinical doctor to detect the presence of epileptiform discharges. Epileptiform discharges are transient waveforms lasting for several tens to hundreds of milliseconds and are mainly divided into seven types. It is important to develop systematic approaches to accurately distinguish these waveforms from normal control ones. This is a difficult task if one wishes to develop first principle rather than black-box based approaches, since clinically used scalp EEGs usually contain a lot of noise and artifacts. To solve this problem, we analyzed 640 multi-channel EEG segments, each 4 long. Among these segments, 540 are short epileptiform discharges, and 100 are from healthy controls. We have proposed two approaches for distinguishing epileptiform discharges from normal EEGs. The first method is based on Signal Range and EEGs' long range correlation properties characterized by the Hurst parameter extracted by applying adaptive fractal analysis (AFA), which can also maximally suppress the effects of noise and various kinds of artifacts. Our second method is based on networks constructed from three aspects of the scalp EEG signals, the Signal Range, the energy of the alpha wave component, and EEG's long range correlation properties. The networks are further analyzed using singular value decomposition (SVD). The square of the first singular value from SVD is used to construct features to distinguish epileptiform discharges from normal controls. Using Random Forest Classifier (RF), our approaches can achieve very high accuracy in distinguishing epileptiform discharges from normal control ones, and thus are very promising to be used clinically. The network-based approach is also used to infer the localizations of each type of epileptiform discharges, and it is found that the sub-networks representing the most likely location of each type of epileptiform discharges are different among the seven types of epileptiform discharges.
癫痫是最常见的脑部疾病之一。临床上，为了证实类似癫痫发作的症状并找到发作定位发作的发作定位，临床医生通常会对脑电图（EEG）数据进行目视检查，以检测是否存在癫痫样放电。癫痫样放电是持续数十至数百毫秒的瞬态波形，主要分为七种类型。开发系统的方法以准确区分这些波形与正常对照波形非常重要。如果想要开发基于第一原理而非黑箱的方法，这是一项艰巨的任务，因为临床使用的头皮脑电图通常包含大量噪声和伪迹。为了解决这个问题，我们分析了640个多通道脑电图片段，每个片段长4秒。在这些片段中，540个是短暂的癫痫样放电，100个来自健康对照。我们提出了两种区分癫痫样放电与正常脑电图的方法。第一种方法基于信号范围和脑电图的长程相关性，其特征在于通过应用自适应分形分析（AFA）提取的赫斯特参数，这也可以最大程度地抑制噪声和各种伪迹的影响。我们的第二种方法基于从头皮脑电图信号的三个方面构建的网络，即信号范围、α波成分的能量和脑电图的长程相关性。使用奇异值分解（SVD）对网络进行进一步分析。SVD的第一个奇异值的平方用于构建区分癫痫样放电与正常对照的特征。使用随机森林分类器（RF），我们的方法在区分癫痫样放电与正常对照方面可以达到非常高的准确率，因此在临床上具有很大的应用潜力。基于网络的方法还用于推断每种癫痫样放电的定位，发现在七种癫痫样放电类型中，代表每种癫痫样放电最可能位置的子网是不同的。
