Epilepsy is a brain disease in nerves which causes sudden seizure, sensations, and once in a while loss of mindfulness. This disorder is difficult to find manually because of its unpredictable nature since it is very hard to treat. The World Health Organization states that fifty million people having this type of disorder worldwide. Automatic detection assumes a significant role in the finding of epilepsy for it can get imperceptible data of Epileptic Electroencephalogram Signals precisely and diminish the burdens of medical field. The Brain’s function is monitored by using these EEG signals electrically. The goal of this paper is to find a classification on Electroencephalogram (EEG) signals using the Bonn University datasets. In order to address this challenge, we propose a new Peak Signal Features (PSF) method which extracts high and low peak features from EEG signals. In addition, Support Vector Machine, Decision Tree and K-Nearest Neighbor are used for classification. Finally, overall accuracy and the Mean Square Error rates of the above three classification methods with proposed method are measured. The experimental result demonstrates the effectiveness of the proposed approach. It also proves that SVM with proposed Peak Signal Features method gives better result than the other methods.

癫痫病是神经的一种脑部疾病，会导致突然的癫痫发作，感觉和偶尔的正念丧失。由于这种疾病难以预测，因此很难手动找到，因为很难治疗。世界卫生组织指出，全世界有五千万人患有这种疾病。自动检测在癫痫的发现中起着重要作用，因为它可以准确地获取癫痫性脑电信号的不可察觉的数据并减轻医疗领域的负担。通过电气使用这些EEG信号来监控大脑的功能。本文的目的是使用波恩大学数据集找到脑电图（EEG）信号的分类。为了应对这一挑战，我们提出了一种新的峰值信号特征（PSF）方法，该方法可从EEG信号中提取高峰值特征和低峰值特征。另外，使用支持向量机，决策树和K最近邻进行分类。最后，测量了上述三种分类方法的总体准确性和均方误差率。实验结果证明了该方法的有效性。这也证明了采用峰值信号特征方法的支持向量机具有比其他方法更好的效果。实验结果证明了该方法的有效性。这也证明了采用峰值信号特征方法的支持向量机具有比其他方法更好的效果。实验结果证明了该方法的有效性。这也证明了采用峰值信号特征方法的支持向量机具有比其他方法更好的效果。