Reliable analysis of electroencephalogram (EEG) signals is crucial that could lead the way to correct diagnostic and therapeutic methods for the treatment of patients with neurological abnormalities, especially epilepsy. This paper presents a novel analysis system for detecting epileptic seizure from EEG signals, which uses statistical features based on optimum allocation technique (OAT) with logistic model trees (LMT). The analysis involves applying the OAT to select representative EEG signals that reflect the entire database. Then, some statistical features are extracted from these EEG signals and the obtained feature set is fed into the LMT classification model to detect epileptic seizure. To test the consistency of the proposed method, all experiments are carried out on a benchmark EEG dataset and repeated twenty times with the same parameters in the detection process, and the average values of the performance parameters are reported. The results show very high detection performances for each class, and also confirm the consistency of the proposed method in the repeating process. The proposed method outperforms some state-of-the-art methods of epileptic EEG signal detection using the same EEG dataset.

中文翻译：

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使用逻辑模型树从EEG信号中检测癫痫发作。

可靠的脑电图（EEG）信号分析至关重要，这可以为纠正具有神经系统异常（尤其是癫痫病）患者的诊断和治疗方法提供指导。本文提出了一种新的分析系统，用于从EEG信号中检测癫痫发作，该系统使用基于最优分配技术（OAT）和逻辑模型树（LMT）的统计特征。该分析涉及应用OAT来选择反映整个数据库的代表性EEG信号。然后，从这些EEG信号中提取一些统计特征，并将获得的特征集输入LMT分类模型以检测癫痫发作。为了测试所提出方法的一致性，所有实验均在基准EEG数据集上进行，并在检测过程中以相同的参数重复进行20次，并报告性能参数的平均值。结果表明每个类别的检测性能都很高，并且也证实了所提出方法在重复过程中的一致性。所提出的方法优于使用相同EEG数据集的一些最新的癫痫EEG信号检测方法。