Electrical activity of the brain reads through the technique called as electroencephalography for brain disorder like epilepsy. Epileptical signal is extracted from EEG signal through characteristics defined by statistical parameter like expected activity measurement, sample entropy and Higuchi fractal dimension as an input to a classifier. This paper works on the classification approach of EEG signal into healthy, inter-ictal and ictal signal using k-nearest neighbor and artificial neural network classifier according to the statistical parameter. Accuracy, sensitivity, selectivity, specificity and average detection rate are the performance parameter derived from both the classifier for comparison between k-NN and ANN classifier and also for detection of epilepsy with reduced sets of parameter.

中文翻译：

---

使用ANN和KNN分类器检测癫痫和EEG分类的统计参数组合

大脑的电活动通过称为脑电图的技术读取，用于治疗癫痫等脑部疾病。通过由预期活动测量、样本熵和 Higuchi 分形维数等统计参数定义的特征从 EEG 信号中提取癫痫信号作为分类器的输入。本文研究了使用k-最近邻和人工神经网络分类器根据统计参数将EEG信号分为健康信号、发作间信号和发作信号的分类方法。准确度、灵敏度、选择性、特异性和平均检测率是来自分类器的性能参数，用于比较 k-NN 和 ANN 分类器，也用于检测具有减少参数集的癫痫。