An EEG Feature-based Diagnosis Model for Epilepsy

摘要：

Electroencephalogram (EEG) is the most important clinical tool in evaluating patients with epilepsy. However, the EEG definite patterns correlated to various types of epilepsy are still unclear. In this paper, six features of EEG signal are extracted to construct an artificial neural network model of classifying controls and patients with epilepsy. The ROC-score (area under curve) of the model is 88.3％. SD of autocorrelation, Hurst indexes, and periodicity have a good capacity in identifying epilepsy.

关键词： electroencephalogram (EEG) features artificial neural network (ANN)

作者: Kun Luo Donghui Luo

作者单位: Department of neurosurgery of the First Affiliated Hospital of Xinjiang medical university, Urumchi Department of internal neurology of the First Affiliated Hospital of Xinjiang medical university, Ur

会议类型: 国际会议

会议名称: The 2010 International Conference on Computer Application and System Modeling(2010计算机应用与系统建模国际会议 ICCASM 2010)

会议地点: 太原

会议语种:英文

页码: 592-594

在线出版日期: 2010-10-22（万方平台首次上网日期，不代表论文的发表时间）