Real-time Inference and Detection of Disruptive EEG Networks for Epileptic Seizures
Real-time Inference and Detection of Disruptive EEG Networks for Epileptic Seizures
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DOI：
10.1038/s41598-020-65401-6
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发表时间：
2020-05-26
影响因子：
4.6
通讯作者：
Li, Jr-Shin
中科院分区：
文献类型：
--
作者：
Bomela, Walter;Wang, Shuo;Li, Jr-Shin
近年来，脑科学和神经医学领域的研究特别关注开发基于机器学习的脑电图（EEG）癫痫发作检测和预测技术。作为一种记录脑电活动的非侵入性监测方法，EEG已被广泛用于实时捕获大脑中破坏性神经元反应的潜在动力学，以在实践中为癫痫发作治疗提供临床指导。在这项研究中，我们引入了一种新的动态学习方法，首先推断出一个时变网络构成的多变量EEG信号，这代表了大脑网络的整体动态，随后量化其拓扑性质，使用图论。我们证明了我们的学习方法的有效性，以检测相对较强的同步（其特征在于代数连接度量）引起的异常神经元放电癫痫发作期间。一个现实的头皮EEG数据库的计算结果显示，检测率为93.6%，假阳性率为0.16每小时（FP/h），此外，我们的方法观察到潜在的癫痫发作前的现象，在某些情况下。
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.