Affiliated with RCfalse
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Epileptic Seizure Detection by Cascading Isolation Forest-Based Anomaly Screening and EasyEnsemble
Guo, Yao1; Jiang, Xinyu1; Tao, Linkai2; Meng, Long1; Dai, Chenyun1; Long, Xi2; Wan, Feng3; Zhang, Yuan4; Van Dijk, Johannes5,7,8; Aarts, Ronald M.5; Chen, Wei6; Chen, Chen6
2022
Source PublicationIEEE Transactions on Neural Systems and Rehabilitation Engineering
ISSN1534-4320
Volume30Pages:915-924
Abstract

The electroencephalogram (EEG), for measuring the electrophysiological activity of the brain, has been widely applied in automatic detection of epilepsy seizures. Various EEG-based seizure detection algorithms have already yielded high sensitivity, but training those algorithms requires a large amount of labelled data. Data labelling is often done with a lot of human efforts, which is very time-consuming. In this study, we propose a hybrid system integrating an unsupervised learning (UL) module and a supervised learning (SL) module, where the UL module can significantly reduce the workload of data labelling. For preliminary seizure screening, UL synthesizes amplitude-integrated EEG (aEEG) extraction, isolation forest-based anomaly detection, adaptive segmentation, and silhouette coefficient-based anomaly detection evaluation. The UL module serves to quickly locate the determinate subjects (seizure segments and seizure-free segments) and the indeterminate subjects (potential seizure candidates). Afterwards, more robust seizure detection for the indeterminate subjects is performed by the SL using an EasyEnsemble algorithm. EasyEnsemble, as a class-imbalance learning method, can potentially decrease the generalization error of the seizure-free segments. The proposed method can significantly reduce the workload of data labelling while guaranteeing satisfactory performance. The proposed seizure detection system is evaluated using the Children's Hospital Boston-Massachusetts Institute of Technology (CHB-MIT) scalp EEG dataset, and it achieves a mean accuracy of 92.62%, a mean sensitivity of 95.55%, and a mean specificity of 92.57%. To the best of our knowledge, this is the first epilepsy seizure detection study employing the integration of both the UL and the SL modules, achieving a competitive performance superior or similar to that of the state-of-the-art methods.

KeywordAeeg Anomaly Detection Eeg Seizure Detection System Supervised Learning Unsupervised Learning
DOI10.1109/TNSRE.2022.3163503
URLView the original
Indexed BySCIE
Language英語English
WOS Research AreaEngineering ; Rehabilitation
WOS SubjectEngineering, Biomedical ; Rehabilitation
WOS IDWOS:000782413000003
Scopus ID2-s2.0-85127473083
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Citation statistics
Cited Times [WOS]:1   [WOS Record]     [Related Records in WOS]
Document TypeJournal article
CollectionDEPARTMENT OF ELECTRICAL AND COMPUTER ENGINEERING
Corresponding AuthorChen, Wei; Chen, Chen
Affiliation1.Center for Intelligent Medical Electronics, School of Information Science and Technology, Fudan University, Shanghai, 200438, China
2.Department of Industrial Design, Eindhoven University of Technology, Eindhoven, 5612, Netherlands
3.Centre for Cognitive and Brain Sciences, Department of Electrical and Computer Engineering, Faculty of Science and Technology, Institute of Collaborative Innovation, University of Macau, 999078, Macao
4.College of Electronic and Information Engineering, Southwest University, Chongqing, 400715, China
5.Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven, 5612, Netherlands
6.Center for Intelligent Medical Electronics, School of Information Science and Technology, Human Phenome Institute, Fudan University, Shanghai, 200438, China
7.Epilepsy Center Kempenhaeghe, AB Heeze, 5590, Netherlands
8.Department of Orthodontics, University of Ulm, Ulm, 89081, Germany
Recommended Citation
GB/T 7714
Guo, Yao,Jiang, Xinyu,Tao, Linkai,et al. Epileptic Seizure Detection by Cascading Isolation Forest-Based Anomaly Screening and EasyEnsemble[J]. IEEE Transactions on Neural Systems and Rehabilitation Engineering,2022,30:915-924.
APA Guo, Yao,Jiang, Xinyu,Tao, Linkai,Meng, Long,Dai, Chenyun,Long, Xi,Wan, Feng,Zhang, Yuan,Van Dijk, Johannes,Aarts, Ronald M.,Chen, Wei,&Chen, Chen.(2022).Epileptic Seizure Detection by Cascading Isolation Forest-Based Anomaly Screening and EasyEnsemble.IEEE Transactions on Neural Systems and Rehabilitation Engineering,30,915-924.
MLA Guo, Yao,et al."Epileptic Seizure Detection by Cascading Isolation Forest-Based Anomaly Screening and EasyEnsemble".IEEE Transactions on Neural Systems and Rehabilitation Engineering 30(2022):915-924.
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