An Electroencephalogram (EEG) is a non-invasive exam that records the electrical activity of the brain. This exam is used to help diagnose conditions such as different brain problems. EEG signals are taken for the purpose of epilepsy detection and with Discrete Wavelet Transform (DWT) and machine learning classifier, they perform epilepsy detection. In Epilepsy seizure detection, mainly machine learning classifiers and statistical features are used. The hidden information in the EEG signal is useful for detecting diseases affecting the brain. Sometimes it is very difficult to identify the minimum changes in the EEG in the time and frequency domains purpose. The DWT can give a good decomposition of the signals in different frequency bands and feature extraction. We use the tri-dimensionality reduction algorithm.; Principal Component Analysis (PCA), Independent Component Analysis (ICA), and Linear Discriminant Analysis (LDA). Finally, features are selected by using a fusion rule and at the last step three different classifiers Support Vector Machine (SVM), Naive Bayes (NB) and K-Nearest-Neighbor(KNN) have been used individually for the classification. The proposed framework is tested on the Bonn dataset and the simulation results provide the accuracy for the combination of LDA and SVM 89.17%, LDA and KNN 80.42%, PCA and NB 89.92%, PCA and SVM 85.58%, PCA and KNN 80.42%, ICA and NB 82.33%, ICA and SVM 90.42%, and ICA and KNN 90%, LDA and NB 100%, accuracy. It shows the sensitivity, specificity, accuracy, Precision, and Recall of 100%, 100%, 100%, 100%, and 100%. This combination of LDA with NB method provides the accuracy of 100% outperforming all existing methods. The results prove the effectiveness of this model.

翻译：脑电图（EEG）是一种记录大脑电活动的非侵入性检查方法，常用于辅助诊断各类脑部疾病。本研究采集EEG信号用于癫痫检测，并通过离散小波变换（DWT）与机器学习分类器实现癫痫发作识别。在癫痫发作检测中，主要采用机器学习分类器与统计特征，EEG信号中的隐藏信息对脑部疾病诊断具有重要价值。然而，在时频域中识别EEG信号的微小变化往往十分困难。DWT能够有效实现信号在不同频段的分解与特征提取。我们采用三种降维算法：主成分分析（PCA）、独立成分分析（ICA）和线性判别分析（LDA）。最后通过融合规则进行特征选择，并分别使用支持向量机（SVM）、朴素贝叶斯（NB）和K近邻（KNN）三种分类器进行独立分类。该框架在Bonn数据集上进行了测试，仿真结果显示：LDA与SVM组合准确率为89.17%，LDA与KNN组合为80.42%，PCA与NB组合为89.92%，PCA与SVM组合为85.58%，PCA与KNN组合为80.42%，ICA与NB组合为82.33%，ICA与SVM组合为90.42%，ICA与KNN组合为90%，而LDA与NB组合准确率达100%。该组合的灵敏度、特异度、准确率、精确率和召回率均为100%，优于现有所有方法，验证了该模型的有效性。