IEEE Trans Biomed Eng. 2019 Mar;66(3):601-608. doi: 10.1109/TBME.2018.2850959. Epub 2018 Jun 27.
Synchronization phenomena of epileptic electroencephalography (EEG) have long been studied. In this study, we aim at investigating the spatial-temporal synchronization pattern in epileptic human brains using the spectral graph theoretic features extracted from scalp EEG and developing an efficient multivariate approach for detecting seizure onsets in real time.
A complex network model is used for representing the recurrence pattern of EEG signals, based on which the temporal synchronization patterns are quantified using the spectral graph theoretic features. Furthermore, a statistical control chart is applied to the extracted features overtime for monitoring the transits from normal to epileptic states in multivariate EEG systems.
Our method is tested on 23 patients from CHB-MIT Scalp EEG database. The results show that the graph theoretic feature yields a high sensitivity ( ∼ 98%) and low latency ( ∼ 6 s) on average, and seizure onsets in 18 patients are 100% detected.
Our approach validates the increased temporal synchronization in epileptic EEG and achieves a comparable detection performance to previous studies.
We characterize the temporal synchronization patterns of epileptic EEG using spectral network metrics. In addition, we found significant changes in temporal synchronization in epileptic EEG, which enable a patient-specific approach for real-time seizure detection for personalized diagnosis and treatment.
癫痫脑电（EEG）的同步现象一直是研究的热点。本研究旨在通过从头皮 EEG 中提取的谱图理论特征来研究癫痫人脑的时空同步模式，并开发一种用于实时检测癫痫发作的高效多变量方法。
使用复杂网络模型来表示 EEG 信号的重现模式，基于此，使用谱图理论特征来量化时间同步模式。此外，随着时间的推移，将统计控制图应用于提取的特征，以监测多变量 EEG 系统中从正常状态到癫痫状态的转变。
我们的方法在来自 CHB-MIT 头皮 EEG 数据库的 23 名患者中进行了测试。结果表明，该图理论特征的平均灵敏度（约 98%）和平均潜伏期（约 6 秒）较高，18 名患者的癫痫发作均被 100%检测到。
我们使用谱网络度量来描述癫痫 EEG 的时间同步模式，并取得了与先前研究相当的检测性能。
我们使用谱图理论特征来描述癫痫 EEG 的时间同步模式。此外，我们发现癫痫 EEG 中的时间同步发生了显著变化，这使得可以针对每个患者进行实时癫痫检测，从而实现个性化诊断和治疗。
