Kabir Enamul, Zhang Yanchun
School of Agricultural, Computational and Environmental Sciences, University of Southern Queensland, Toowoomba, QLD, Australia.
Centre for Applied Informatics, College of Engineering and Science, Victoria University, Melbourne, Australia.
Brain Inform. 2016 Jun;3(2):93-100. doi: 10.1007/s40708-015-0030-2. Epub 2016 Jan 21.
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.
对脑电图（EEG）信号进行可靠分析至关重要，这可能为治疗神经功能异常患者，尤其是癫痫患者，找到正确的诊断和治疗方法指明方向。本文提出了一种用于从EEG信号中检测癫痫发作的新型分析系统，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。分析过程包括应用OAT来选择能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到LMT分类模型中以检测癫痫发作。为了测试所提方法的一致性，所有实验均在一个基准EEG数据集上进行，并且在检测过程中使用相同参数重复进行二十次，报告性能参数的平均值。结果显示每个类别都具有非常高的检测性能，同时也证实了所提方法在重复过程中的一致性。使用相同的EEG数据集时，所提方法优于一些癫痫EEG信号检测的现有先进方法。
