基于小波熵特征的递归神经网络自动检测癫痫发作

TENCON 2008 - 2008 IEEE Region 10 Conference Pub Date : 2008-11-01 DOI:10.1109/TENCON.2008.4766836

S. Pravin Kumar, N. Sriraam, P. Benakop

{"title":"基于小波熵特征的递归神经网络自动检测癫痫发作","authors":"S. Pravin Kumar, N. Sriraam, P. Benakop","doi":"10.1109/TENCON.2008.4766836","DOIUrl":null,"url":null,"abstract":"Electroencephalograms (EEG) are the brain signals that provide us the valuable information about the normal or epileptic state of the brain. In this paper the EEG signals were characterized by wavelet, sample and spectral entropy approach and the recurrent neural network classifier is used for the automated detection of epileptic seizures.","PeriodicalId":22230,"journal":{"name":"TENCON 2008 - 2008 IEEE Region 10 Conference","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2008-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"25","resultStr":"{\"title\":\"Automated detection of epileptic seizures using wavelet entropy feature with recurrent neural network classifier\",\"authors\":\"S. Pravin Kumar, N. Sriraam, P. Benakop\",\"doi\":\"10.1109/TENCON.2008.4766836\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Electroencephalograms (EEG) are the brain signals that provide us the valuable information about the normal or epileptic state of the brain. In this paper the EEG signals were characterized by wavelet, sample and spectral entropy approach and the recurrent neural network classifier is used for the automated detection of epileptic seizures.\",\"PeriodicalId\":22230,\"journal\":{\"name\":\"TENCON 2008 - 2008 IEEE Region 10 Conference\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2008-11-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"25\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"TENCON 2008 - 2008 IEEE Region 10 Conference\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/TENCON.2008.4766836\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"TENCON 2008 - 2008 IEEE Region 10 Conference","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/TENCON.2008.4766836","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}

引用次数: 25

摘要

脑电图(EEG)是一种大脑信号，它为我们提供了关于大脑正常或癫痫状态的宝贵信息。本文采用小波、样本和谱熵方法对脑电图信号进行表征，并采用递归神经网络分类器对癫痫发作进行自动检测。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

查看原文

微信好友朋友圈 QQ好友复制链接

本刊更多论文

Automated detection of epileptic seizures using wavelet entropy feature with recurrent neural network classifier

Electroencephalograms (EEG) are the brain signals that provide us the valuable information about the normal or epileptic state of the brain. In this paper the EEG signals were characterized by wavelet, sample and spectral entropy approach and the recurrent neural network classifier is used for the automated detection of epileptic seizures.

求助全文

通过发布文献求助，成功后即可免费获取论文全文。去求助

来源期刊

TENCON 2008 - 2008 IEEE Region 10 Conference

自引率

0.00%

发文量