|本期目录/Table of Contents|

[1]韦晓燕,周霖,周毅△.基于脑电信号提取与识别的癫痫预测研究*[J].生物医学工程研究,2018,04:531-535.
 WEI Xiaoyan,ZHOU Lin,ZHOU Yi.Epileptic prediction based on electroencephalography extraction and recognition[J].Journal of Biomedical Engineering Research,2018,04:531-535.
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基于脑电信号提取与识别的癫痫预测研究*(PDF)

《生物医学工程研究》[ISSN:1006-6977/CN:61-1281/TN]

期数:
2018年04期
页码:
531-535
栏目:
出版日期:
2018-12-25

文章信息/Info

Title:
Epileptic prediction based on electroencephalography extraction and recognition
文章编号:
1672-6278 (2018)04-0531-05
作者:
韦晓燕1周霖2周毅1△
1. 中山大学中山医学院,广州510080;2. 中山大学 数据科学与计算机学院,广州510006
Author(s):
WEI Xiaoyan1 ZHOU Lin2 ZHOU Yi1
1.Department of Biomedical Engineering,Medical College,Sun Yat - sen University,Guangzhou 510080,Guangdong,China;2. Software Engineering ,School of Computer and Data Science, Sun Yat - sen University,Guangzhou 510006
关键词:
癫痫发作预测信号处理特征提取机器学习
Keywords:
EpilepsySeizure predictionSignal processingFeature extractionMachine learning
分类号:
R318
DOI:
10.19529/j.cnki.1672-6278.2018.04.31
文献标识码:
A
摘要:
癫痫发作预测的主要研究为检测发作前期状态。尽管癫痫发作预测研究众多,其准确率也在不断提升,但其应用于临床分析还为时过早,主要原因在于缺乏令人信服的统计检验和实验可重复性。基于此,本研究回顾了癫痫预测算法研究框架,从信号采集到性能评价等,着重于信号处理和信号识别,讨论提出优化癫痫预测模型框架,用于实现更准确可靠的发作预测。
Abstract:
Seizure detection employs algorithms that aim to detect preictal state. Although efforts for better prediction have been made, the translation of current approaches to clinical applications is still not possible due to the absence of statistical validation and reproducibility. Analysis and algorithmic studies provide evidence that transition to the ictal state is not random, with build-up leading to seizures, discussing a framework for improving and optimizing the epilepsy prediction model .

参考文献/References

备注/Memo

备注/Memo:
(收稿日期:2018-02-24) 国家自然科学基金资助项目(61876194);国家重点研发计划专项(2016YFC0901602,2018YFC0116902); 广东大数据科学中心联合基金项目(U1611261)。△通信作者Email:zhouyi@sysu.edu.cn
更新日期/Last Update: 2019-01-30