Kaya Yılmaz, Ertuğrul Ömer Faruk
Department of Computer Engineering, Siirt University, Siirt, Turkey.
Department of Electrical and Electronics Engineering, Batman University, Batman, Turkey.
Australas Phys Eng Sci Med. 2018 Sep;41(3):721-730. doi: 10.1007/s13246-018-0669-0. Epub 2018 Aug 16.
Epilepsy is one of the most common neurological disorders. Electroencephalogram (EEG) signals are generally employed in diagnosing epilepsy. Therefore, extracting relevant features from EEG signals is one of the major tasks in an accurate diagnosis. In this study, the local ternary patterns, which is an image processing method, was improved in order to extract robust features from epileptic EEG signals. The EEG signals that were recorded by the Department of Etymology in the Bonn University were employed in the evaluation and validation of the proposed approach. Low and up features, which were extracted by the proposed one-dimensional ternary patterns, were classified by some machine learning methods such that support vector machine, functional trees, random forest (RF), Bayes networks (BayesNet), and artificial neural network, while the highest accuracies were obtained by RF. Achieved accuracies were found successful according to the current literature.
癫痫是最常见的神经系统疾病之一。脑电图（EEG）信号通常用于癫痫诊断。因此，从EEG信号中提取相关特征是准确诊断的主要任务之一。在本研究中，为了从癫痫EEG信号中提取鲁棒特征，对作为一种图像处理方法的局部三元模式进行了改进。采用波恩大学词源学系记录的EEG信号对所提方法进行评估和验证。通过所提的一维三元模式提取的低频和高频特征，采用支持向量机、功能树、随机森林（RF）、贝叶斯网络（BayesNet）和人工神经网络等一些机器学习方法进行分类，其中RF获得了最高准确率。根据当前文献，所取得的准确率是成功的。
