Epilepsy affects 50 million people worldwide and is one of the most common serious neurological disorders. Seizure detection and classification is a valuable tool for diagnosing and maintaining the condition. An automated classification algorithm will allow for accurate diagnosis. Utilising the Temple University Hospital (TUH) Seizure Corpus, six seizure types are compared; absence, complex partial, myoclonic, simple partial, tonic and tonic- clonic models. This study proposes a method that utilises unique features with a novel parallel classifier - Parallel Genetic Naive Bayes (NB) Seizure Classifier (PGNBSC). The PGNBSC algorithm searches through the features and by reclassifying the data each time, the algorithm will create a matrix for optimum search criteria. Ictal states from the EEGs are segmented into 1.8 s windows, where the epochs are then further decomposed into 13 different features from the first intrinsic mode function (IMF). The features are compared using an original NB classifier in the first model. This is improved upon in a second model by using a genetic algorithm (Binary Grey Wolf Optimisation, Option 1) with a NB classifier. The third model uses a combination of the simple partial and complex partial seizures to provide the highest classification accuracy for each of the six seizures amongst the three models (20%, 53%, and 85% for first, second, and third model, respectively).

翻译：缉获检测和分类是诊断和保持该条件的宝贵工具。自动分类算法将允许进行准确诊断。使用圣殿大学医院(TUH)查获科布斯,将6种缉获类型进行比较;缺乏、复杂、局部、近距离、简单局部、通俗和通尼止血模型;本研究报告建议采用一种方法,利用与新颖平行分类器 -- -- 平行遗传甲壳类(NBBSC)的独有特征。PGNBSC算法搜索通过特征和每次对数据进行重新分类,算法将为最佳搜索标准创建矩阵。从EEEGs的Ictal States被分割到1.8秒的窗口,然后将Epochs进一步分解成与第一个内在模式功能(IMFIF)的13个不同特征。这些特征在第一个模型中使用了原始的NBS分类器(NB),在第二个模型中加以改进,在第三个模型中使用了遗传算法(Bary Grey Wolf Oppimimimation, 53 选择1), 提供了每6个最精确的模型(20%) 和最精确的缉获的分类。