Seizure is an abnormal electrical activity of the brain. Neurologists can diagnose the seizure using several methods such as neurological examination, blood tests, computerized tomography (CT), magnetic resonance imaging (MRI) and electroencephalogram (EEG). Medical data, such as the EEG signal, usually includes a number of features and attributes that do not contains important information. This paper proposes an automatic seizure classification system based on extracting the most significant EEG features for seizure diagnosis. The proposed algorithm consists of five steps. The first step is the channel selection to minimize dimensionality by selecting the most affected channels using the variance parameter. The second step is the feature extraction to extract the most relevant features, 11 features, from the selected channels. The third step is to average the 11 features extracted from each channel. Next, the fourth step is the classification of the average features using the classification step. Finally, cross-validation and testing the proposed algorithm by dividing the dataset into training and testing sets. This paper presents a comparative study of seven classifiers. These classifiers were tested using two different methods: random case testing and continuous case testing. In the random case process, the KNN classifier had greater precision, specificity, positive predictability than the other classifiers. Still, the ensemble classifier had a higher sensitivity and a lower miss-rate (2.3%) than the other classifiers. For the continuous case test method, the ensemble classifier had higher metric parameters than the other classifiers. In addition, the ensemble classifier was able to detect all seizure cases without any mistake.

中文翻译：

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基于脑电信号特征提取和通道选择的癫痫发作自动诊断系统

癫痫发作是大脑的异常电活动。神经科医生可以使用多种方法来诊断癫痫发作，例如神经学检查，血液检查，计算机断层扫描（CT），磁共振成像（MRI）和脑电图（EEG）。医学数据（例如EEG信号）通常包含许多不包含重要信息的特征和属性。本文提出了一种自动癫痫发作分类系统，该系统基于提取最重要的脑电图特征进行癫痫发作诊断。所提出的算法包括五个步骤。第一步是通过使用方差参数选择受影响最大的通道来最小化尺寸的通道选择。第二步是特征提取，从所选通道中提取最相关的特征11个特征。第三步是对从每个通道提取的11个特征求平均值。接下来，第四步是使用分类步骤对平均特征进行分类。最后，通过将数据集分为训练集和测试集来交叉验证和测试提出的算法。本文提出了对七个分类器的比较研究。这些分类器使用两种不同的方法进行了测试：随机案例测试和连续案例测试。在随机案例过程中，KNN分类器比其他分类器具有更高的精度，特异性和积极的可预测性。尽管如此，集成分类器仍比其他分类器具有更高的灵敏度和更低的未命中率（2.3％）。对于连续案例测试方法，集成分类器具有比其他分类器更高的度量参数。此外，