CASIA OpenIR  > 中国科学院分子影像重点实验室
Automated Epileptic Seizure Detection in Scalp EEG Based on Spatial-Temporal Complexity
Zhu, Xinzhong1,2,3; Xu, Huiying3; Zhao, Jianmin3; Tian, Jie2
Source PublicationCOMPLEXITY
2017
SubtypeArticle
AbstractEpilepsy is a group of neurological disorders characterized by epileptic seizures, wherein electroencephalogram (EEG) is one of the most common technologies used to diagnose, monitor, and manage patients with epilepsy. A large number of EEGs have been recorded in clinical applications, which leads to visual inspection of huge volumes of EEG not routinely possible. Hence, automated detection of epileptic seizure has become a goal of many researchers for a long time. A novel method is therefore proposed to construct a patient-specific detector based on spatial-temporal complexity analysis, involving two commonly used entropy-based complexity analysis methods, which are permutation entropy (PE) and sample entropy (SE). The performance of spatial-temporal complexity method is evaluated on a shared dataset. Results suggest that the proposed epilepsy detectors achieve promising performance: the average sensitivities of PE and SE in 23 patients are 99% and 96.6%, respectively. Moreover, both methods can accurately recognize almost all the seizure-free EEG. The proposed method not only obtains a high accuracy rate but also meets the real-time requirements for its application on seizure detection, which suggests that the proposed method has the potential of detecting epileptic seizures in real time.
WOS HeadingsScience & Technology ; Physical Sciences
DOI10.1155/2017/5674392
WOS KeywordPERMUTATION ENTROPY ; SAMPLE ENTROPY ; APPROXIMATE ENTROPY ; ABSENCE SEIZURES ; TIME-SERIES ; BRAIN ; CHAOS
Indexed BySCI
Language英语
Funding OrganizationOpening Fund of Zhejiang Provincial Top Key Discipline of Computer Science and Technology at Zhejiang Normal University
WOS Research AreaMathematics ; Science & Technology - Other Topics
WOS SubjectMathematics, Interdisciplinary Applications ; Multidisciplinary Sciences
WOS IDWOS:000419499600001
Citation statistics
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/21933
Collection中国科学院分子影像重点实验室
Affiliation1.Xidian Univ, Sch Life Sci & Technol, Xian, Shaanxi, Peoples R China
2.Chinese Acad Sci, CAS Key Lab Mol Imaging, Inst Automat, Beijing, Peoples R China
3.Zhejiang Normal Univ, Coll Math Phys & Informat Engn, Jinhua, Zhejiang, Peoples R China
Recommended Citation
GB/T 7714
Zhu, Xinzhong,Xu, Huiying,Zhao, Jianmin,et al. Automated Epileptic Seizure Detection in Scalp EEG Based on Spatial-Temporal Complexity[J]. COMPLEXITY,2017.
APA Zhu, Xinzhong,Xu, Huiying,Zhao, Jianmin,&Tian, Jie.(2017).Automated Epileptic Seizure Detection in Scalp EEG Based on Spatial-Temporal Complexity.COMPLEXITY.
MLA Zhu, Xinzhong,et al."Automated Epileptic Seizure Detection in Scalp EEG Based on Spatial-Temporal Complexity".COMPLEXITY (2017).
Files in This Item:
There are no files associated with this item.
Related Services
Recommend this item
Bookmark
Usage statistics
Export to Endnote
Google Scholar
Similar articles in Google Scholar
[Zhu, Xinzhong]'s Articles
[Xu, Huiying]'s Articles
[Zhao, Jianmin]'s Articles
Baidu academic
Similar articles in Baidu academic
[Zhu, Xinzhong]'s Articles
[Xu, Huiying]'s Articles
[Zhao, Jianmin]'s Articles
Bing Scholar
Similar articles in Bing Scholar
[Zhu, Xinzhong]'s Articles
[Xu, Huiying]'s Articles
[Zhao, Jianmin]'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.