Automated Epileptic Seizure Detection in EEGs Using Increment Entropy

被引：0
|
作者：
Liu, Xiaofeng [1 ]
Jiang, Aimin
Xu, Ning
机构：
[1] Hohai Univ, Coll IoT Engn, Changzhou 213022, Peoples R China
来源：
2017 IEEE 30TH CANADIAN CONFERENCE ON ELECTRICAL AND COMPUTER ENGINEERING (CCECE) | 2017年
基金：
中国国家自然科学基金;
关键词：
ARTIFICIAL NEURAL-NETWORKS; APPROXIMATE ENTROPY; PERMUTATION ENTROPY; TIME-SERIES;
D O I：
暂无
中图分类号：
TP301 [理论、方法];
学科分类号：
081202 ;
摘要：
This paper presents an automated method for seizure detection in EEGs using an increment entropy (IncrEn) and support vector machines (SVMs). The IncrEn is a measure of the complexity of time series, which characterizes both the permutation of values and the temporal order of values. The IncrEn is used to extract features of epileptic EEGs and normal EEGs. The SVMs are employed to classify seizure EEGs from non-seizure ones. The maximum accuracy achieves 97.32%. The maximum sensitivity and the maximum specificity are 95.34% and 99.30%, respectively. The results indicate our approach using the IncrEn and SVMs is an effective tool to detect EEG seizure.
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页数：4
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