Time-frequency extraction of EEG spike events for seizure detection in neonate

H. Hassanpour, William Williams, M. Mesbah, B. Boashash
{"title":"Time-frequency extraction of EEG spike events for seizure detection in neonate","authors":"H. Hassanpour, William Williams, M. Mesbah, B. Boashash","doi":"10.1109/ISSPA.2001.949823","DOIUrl":null,"url":null,"abstract":"There are a number of approaches for analysing EEG signals in the time, frequency, and time-frequency domains. However due to the nonstationarity of the EEG signals, the time-frequency methods proved to be superior. This paper presents a new method for the detection of newborn EEG seizure activity in the time-frequency domain. The proposed approach utilises 30-second epochs of EEG signal and analyses one frequency slice of their time-frequency representations at about 75 Hz. Spiking activity in these high frequency slices are used to distinguish between seizure and nonseizure activities. Using histograms of intervals between successive spikes, we were able to show the dramatic difference between spikes originating from seizure and those from nonseizure. This finding led to this novel method of seizure detection in newborn babies.","PeriodicalId":236050,"journal":{"name":"Proceedings of the Sixth International Symposium on Signal Processing and its Applications (Cat.No.01EX467)","volume":"130 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"16","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the Sixth International Symposium on Signal Processing and its Applications (Cat.No.01EX467)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ISSPA.2001.949823","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 16

Abstract

There are a number of approaches for analysing EEG signals in the time, frequency, and time-frequency domains. However due to the nonstationarity of the EEG signals, the time-frequency methods proved to be superior. This paper presents a new method for the detection of newborn EEG seizure activity in the time-frequency domain. The proposed approach utilises 30-second epochs of EEG signal and analyses one frequency slice of their time-frequency representations at about 75 Hz. Spiking activity in these high frequency slices are used to distinguish between seizure and nonseizure activities. Using histograms of intervals between successive spikes, we were able to show the dramatic difference between spikes originating from seizure and those from nonseizure. This finding led to this novel method of seizure detection in newborn babies.
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
脑电图尖峰事件的时频提取用于新生儿癫痫发作检测
在时域、频域和时频域分析脑电图信号有许多方法。然而,由于脑电信号的非平稳性,时频方法被证明是优越的。本文提出了一种在时频域检测新生儿脑电图发作活动的新方法。该方法利用30秒周期的脑电图信号,在约75hz的频率下分析其时频表示的一个频率切片。这些高频切片中的尖峰活动被用来区分癫痫发作和非癫痫发作。使用连续尖峰之间的间隔直方图,我们能够显示由癫痫发作和非癫痫发作引起的尖峰之间的巨大差异。这一发现导致了这种新方法的癫痫发作检测新生儿。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
自引率
0.00%
发文量
0
期刊最新文献
Large dynamic range time-frequency signal analysis with application to helicopter Doppler radar data Statistical analysis of neural network modeling and identification of nonlinear systems with memory Design of oversampled uniform DFT filter banks with reduced inband aliasing and delay constraints Identification of DCT signs for sub-block coding Skin color detection for face localization in human-machine communications
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1