Recent studies in brain science and neurological medicine paid a particular attention to develop machine learning-based techniques for the detection and prediction of epileptic seizures with electroencephalogram (EEG). As a noninvasive monitoring method to record brain electrical activities, EEG has been widely used for capturing the underlying dynamics of disruptive neuronal responses across the brain in real-time to provide clinical guidance in support of epileptic seizure treatments in practice. In this study, we introduce a novel dynamic learning method that first infers a time-varying network constituted by multivariate EEG signals, which represents the overall dynamics of the brain network, and subsequently quantifies its topological property using graph theory. We demonstrate the efficacy of our learning method to detect relatively strong synchronization (characterized by the algebraic connectivity metric) caused by abnormal neuronal firing during a seizure onset. The computational results for a realistic scalp EEG database show a detection rate of 93.6% and a false positive rate of 0.16 per hour (FP/h); furthermore, our method observes potential pre-seizure phenomena in some cases.

脑科学和神经医学方面的最新研究特别重视开发基于机器学习的技术，用于通过脑电图（EEG）检测和预测癫痫发作。作为记录脑电活动的一种非侵入性监测方法，脑电图已广泛用于实时捕获整个大脑中破坏性神经元反应的基本动态，以提供临床指导以支持实践中的癫痫发作治疗。在这项研究中，我们介绍了一种新颖的动态学习方法，该方法首先推断出由多元EEG信号构成的时变网络，该网络代表了大脑网络的整体动态，然后使用图论对其进行了量化。我们证明了我们的学习方法检测癫痫发作期间由异常神经元放电引起的相对较强的同步性（由代数连接性度量表征）的功效。真实头皮脑电图数据库的计算结果显示检出率为93.6％，假阳性率为每小时0.16（FP / h）；此外，我们的方法在某些情况下观察到潜在的癫痫发作前现象。