Networks are naturally occurring phenomena that are studied across many disciplines. The topological features of a network can provide insight into the dynamics of a system as it evolves, and can be used to predict changes in state. The brain is a complex network whose temporal and spatial behavior can be measured using electroencephalography (EEG). This data can be reconstructed to form a family of graphs that represent the state of the brain over time, and the evolution of these graphs can be used to predict changes in brain states, such as the transition from preictal to ictal in patients with epilepsy. This research proposes objective indications of seizure onset observed from minimally invasive scalp EEG. The approach considers the brain as a complex nonlinear dynamical system whose state can be derived through time-delay embedding of the EEG data and characterized to determine change in brain dynamics related to the preictal state. This method targets phase-space graph spectra as biomarkers for seizure prediction, correlates historical degrees of change in spectra, and makes accurate prediction of seizure onset. A significant trend of normalized dissimilarity over time indicates a departure from the norm, and thus a change in state. Our methods show high sensitivity (90–100%) and specificity (90%) on 241 h of scalp EEG training data, and sensitivity and specificity of 70%–90% on test data. Moreover, the algorithm was capable of processing 12.7 min of data per second on an Intel Core i3 CPU in Matlab, showing that real-time analysis is viable.

中文翻译：

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通过相空间图的光谱差异预测大脑动力学中的状态转换。

网络是自然现象，需要许多学科的研究。网络的拓扑功能可以洞悉系统在发展过程中的动态，并可以用来预测状态的变化。大脑是一个复杂的网络，其时空行为可以使用脑电图（EEG）进行测量。可以重建这些数据以形成代表大脑随时间变化的一系列图形，这些图形的演变可以用于预测大脑状态的变化，例如癫痫患者从发作期到发作期的转变。这项研究提出了从微创头皮脑电图观察到的癫痫发作的客观指征。该方法将大脑视为复杂的非线性动力学系统，其状态可以通过对EEG数据进行时延嵌入来推导，并可以确定与发作前状态有关的大脑动力学变化。该方法将相空间图谱作为癫痫发作预测的生物标记物，关联谱图的历史变化程度，并对癫痫发作进行准确预测。随着时间的过去，归一化差异的显着趋势表明偏离规范，因此状态发生了变化。我们的方法对241 h头皮脑电图训练数据显示出高灵敏度（90–100％）和特异性（90％），对测试数据显示出70％–90％的灵敏度和特异性。此外，该算法能够在Matlab的Intel Core i3 CPU上每秒处理12.7分钟的数据，这表明实时分析是可行的。