OPT OpenIR  > 光学影像学习与分析中心
Detection Analysis of Epileptic EEG Using a Novel Random Forest Mode Combined With Grid Search Optimization
Wang, Xiashuang1,2; Gong, Guanghong2; Li, Ni1,2; Qiu, Shi3
Department光学影像学习与分析中心
2019-02-21
Source PublicationFRONTIERS IN HUMAN NEUROSCIENCE
ISSN1662-5161
Volume13
Contribution Rank3
Abstract

In the automatic detection of epileptic seizures, the monitoring of critically ill patients with time varying EEG signals is an essential procedure in intensive care units. There is an increasing interest in using EEG analysis to detect seizure, and in this study we aim to get a better understanding of how to visualize the information in the EEG time-frequency feature, and design and train a novel random forest algorithm for EEG decoding, especially for multiple-levels of illness. Here, we propose an automatic detection framework for epileptic seizure based on multiple time-frequency analysis approaches; it involves a novel random forest model combined with grid search optimization. The short-time Fourier transformation visualizes seizure features after normalization. The dimensionality of features is reduced through principal component analysis before feeding them into the classification model. The training parameters are optimized using grid search optimization to improve detection performance and diagnostic accuracy by in the recognition of three different levels epileptic of conditions (healthy subjects, seizure-free intervals, seizure activity). Our proposed model was used to classify 500 samples of raw EEG data, and multiple cross-validations were adopted to boost the modeling accuracy. Experimental results were evaluated by an accuracy, a confusion matrix, a receiver operating characteristic curve, and an area under the curve. The evaluations indicated that our model achieved the more effective classification than some previous typical methods. Such a scheme for computer-assisted clinical diagnosis of seizures has a potential guiding significance, which not only relieves the suffering of patient with epilepsy to improve quality of life, but also helps neurologists reduce their workload.

Keywordcontinuous electroencephalography grid search optimization random forest epileptic seizure detection simulation model
DOI10.3389/fnhum.2019.00052
Indexed BySCI
Language英语
WOS IDWOS:000459301200002
PublisherFRONTIERS MEDIA SA
Citation statistics
Document Type期刊论文
Identifierhttp://ir.opt.ac.cn/handle/181661/31192
Collection光学影像学习与分析中心
Corresponding AuthorLi, Ni
Affiliation1.Beihang Univ, State Key Lab Virtual Real Technol & Syst, Beijing, Peoples R China
2.Beihang Univ, Automat Sci & Elect Engn, Beijing, Peoples R China
3.Chinese Acad Sci, Xian Inst Opt & Precis Mech, Xian, Shaanxi, Peoples R China
Recommended Citation
GB/T 7714
Wang, Xiashuang,Gong, Guanghong,Li, Ni,et al. Detection Analysis of Epileptic EEG Using a Novel Random Forest Mode Combined With Grid Search Optimization[J]. FRONTIERS IN HUMAN NEUROSCIENCE,2019,13.
APA Wang, Xiashuang,Gong, Guanghong,Li, Ni,&Qiu, Shi.(2019).Detection Analysis of Epileptic EEG Using a Novel Random Forest Mode Combined With Grid Search Optimization.FRONTIERS IN HUMAN NEUROSCIENCE,13.
MLA Wang, Xiashuang,et al."Detection Analysis of Epileptic EEG Using a Novel Random Forest Mode Combined With Grid Search Optimization".FRONTIERS IN HUMAN NEUROSCIENCE 13(2019).
Files in This Item:
File Name/Size DocType Version Access License
Detection Analysis o(3280KB)期刊论文出版稿限制开放CC BY-NC-SAApplication Full Text
Related Services
Recommend this item
Bookmark
Usage statistics
Export to Endnote
Google Scholar
Similar articles in Google Scholar
[Wang, Xiashuang]'s Articles
[Gong, Guanghong]'s Articles
[Li, Ni]'s Articles
Baidu academic
Similar articles in Baidu academic
[Wang, Xiashuang]'s Articles
[Gong, Guanghong]'s Articles
[Li, Ni]'s Articles
Bing Scholar
Similar articles in Bing Scholar
[Wang, Xiashuang]'s Articles
[Gong, Guanghong]'s Articles
[Li, Ni]'s Articles
Terms of Use
No data!
Social Bookmark/Share
All comments (0)
No comment.
 

Items in the repository are protected by copyright, with all rights reserved, unless otherwise indicated.