The automatic detection of seizures bears a considerable significance in epileptic diagnosis as it can efficiently lead to a considerable reduction of the workload of the medical staff. The present study aims at automatic detecting epileptic seizures in epileptic rats. To this end, seizures were induced in rats implementing the pentylenetetrazole model, with the electrocorticogram (ECoG) signals during, before and after the seizure periods being recorded. For this purpose, five algorithms for transforming time series into complex networks based on visibility graph (VG) algorithm were used. In this study, VG based methods were used for the first time to analyze ECoG signals in rats. Afterward, Standard measures in network science (graph properties) were made to examine the topological structure of these networks produced on the basis of ECoG signals. Then these measures were given to a classifier as input features so that the ECoG signals could be classified into seizure periods and seizure-free periods. Artificial Neural Network, considered a popular classifier, was used in this work. The experimental results showed that the method managed to detect epileptic seizure in rats with a high accuracy of 92.13%. Our proposed method was also applied to the recorded EEG signals from Bonn database to show the efficiency of the proposed method for human seizure detection.

中文翻译：

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基于复杂网络的ECoG信号模型，用于检测大鼠诱发的癫痫发作。

癫痫发作的自动检测在癫痫诊断中具有相当重要的意义，因为它可以有效地大大减少医务人员的工作量。本研究旨在自动检测癫痫大鼠的癫痫发作。为此，在实施戊烯四唑模型的大鼠中诱发癫痫发作，并在癫痫发​​作期间，之前和之后记录脑电图（ECoG）信号。为此，使用了五种基于可见性图（VG）算法将时间序列转换为复杂网络的算法。在这项研究中，基于VG的方法首次用于分析大鼠的ECoG信号。之后，在网络科学（图形属性）中采取了标准措施来检查基于ECoG信号生成的这些网络的拓扑结构。然后将这些措施作为输入特征提供给分类器，以便将ECoG信号分为癫痫发作期和无癫痫发作期。人工神经网络被认为是一种流行的分类器，用于这项工作。实验结果表明，该方法能够以92.13％的高精度检测大鼠的癫痫发作。我们提出的方法还应用于波恩数据库中记录的脑电信号，以显示提出的方法用于人类癫痫发作检测的效率。人工神经网络被认为是一种流行的分类器，用于这项工作。实验结果表明，该方法能够以92.13％的高精度检测大鼠的癫痫发作。我们提出的方法还应用于波恩数据库中记录的脑电信号，以显示提出的方法用于人类癫痫发作检测的效率。人工神经网络被认为是一种流行的分类器，用于这项工作。实验结果表明，该方法能够以92.13％的高精度检测大鼠的癫痫发作。我们提出的方法还应用于波恩数据库中记录的脑电信号，以显示提出的方法用于人类癫痫发作检测的效率。