DocumentCode
2813379
Title
An EEG feature-based diagnosis model for epilepsy
Author
Luo, Kun ; Luo, Donghui
Author_Institution
Dept. of Neurosurg. of the First Affiliated Hosp., Xinjiang Med. Univ., Urumchi, China
Volume
8
fYear
2010
fDate
22-24 Oct. 2010
Abstract
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.
Keywords
diseases; electroencephalography; feature extraction; medical signal processing; neural nets; patient diagnosis; sensitivity analysis; EEG; Hurst indexes; ROC; area under curve; artificial neural network model; autocorrelation; diagnosis; electroencephalogram; epilepsy; feature extraction; periodicity; artificial neural network (ANN); electroencephalogram (EEG); features;
fLanguage
English
Publisher
ieee
Conference_Titel
Computer Application and System Modeling (ICCASM), 2010 International Conference on
Conference_Location
Taiyuan
Print_ISBN
978-1-4244-7235-2
Electronic_ISBN
978-1-4244-7237-6
Type
conf
DOI
10.1109/ICCASM.2010.5619259
Filename
5619259
Link To Document