School of Biology and Engineering, Guizhou Medical University, Guiyang, Guizhou Province, China.
Department of Neurology, Affiliated Hospital of Guizhou Medical University, Guiyang, Guizhou Province, China.
J Healthc Eng. 2018 Jul 2;2018:8102597. doi: 10.1155/2018/8102597. eCollection 2018.
As a brain disorder, epilepsy is characterized with abnormal hypersynchronous neural firings. It is known that seizures initiate and propagate in different brain regions. Long-term intracranial multichannel electroencephalography (EEG) reflects broadband ictal activity under seizure occurrence. Network-based techniques are efficient in discovering brain dynamics and offering finger-print features for specific individuals. In this study, we adopt link prediction for proposing a novel workflow aiming to quantify seizure dynamics and uncover pathological mechanisms of epilepsy. A dataset of EEG signals was enrolled that recorded from 8 patients with 3 different types of pharmocoresistant focal epilepsy. Weighted networks are obtained from phase locking value (PLV) in subband EEG oscillations. Common neighbor (CN), resource allocation (RA), Adamic-Adar (AA), and Sorenson algorithms are brought in for link prediction performance comparison. Results demonstrate that RA outperforms its rivals. Similarity, matrix was produced from the RA technique performing on EEG networks later. Nodes are gathered to form sequences by selecting the ones with the highest similarity. It is demonstrated that variations are in accordance with seizure attack in node sequences of gamma band EEG oscillations. What is more, variations in node sequences monitor the total seizure journey including its initiation and termination.
作为一种脑部疾病，癫痫的特征是异常的超同步神经放电。众所周知，癫痫发作是在不同的脑区开始和传播的。长期的颅内多通道脑电图（EEG）反映了发作期间的宽带发作活动。基于网络的技术在发现大脑动力学和为特定个体提供指纹特征方面非常有效。在这项研究中，我们采用链路预测来提出一种新的工作流程，旨在量化癫痫发作的动力学，并揭示癫痫的病理机制。我们使用了一个 EEG 信号数据集，该数据集记录了 8 名患有 3 种不同类型药物难治性局灶性癫痫的患者。从子带 EEG 振荡的锁相值（PLV）中获得加权网络。采用共同邻居（CN）、资源分配（RA）、Adamic-Adar（AA）和 Sorenson 算法进行链路预测性能比较。结果表明，RA 优于其他算法。然后，我们使用 RA 技术对 EEG 网络进行处理，生成相似度矩阵。通过选择相似度最高的节点，将节点聚集形成序列。结果表明，在伽马波段 EEG 振荡的节点序列中，变化与癫痫发作一致。更重要的是，节点序列的变化可以监测整个癫痫发作过程，包括起始和终止。
