Online Epileptic Seizure Detection: Statistical Signal Processing Based on Multichannel Electroencephalogram Sensors
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Kim, Hyunchul | - |
dc.contributor.author | Kim, Jungsuk | - |
dc.date.available | 2020-02-27T11:42:25Z | - |
dc.date.created | 2020-02-06 | - |
dc.date.issued | 2018-02 | - |
dc.identifier.issn | 2156-7018 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/gachon/handle/2020.sw.gachon/4073 | - |
dc.description.abstract | This paper presents the framework for an online algorithm for epileptic seizure detection based on component and discriminant analysis combined with a Bayesian decision approach. In our approach, feature vectors representing ictal, interictal, and normal brain activity are extracted from multichannel electrocorticographic (ECoG) sensor data sets based on autoregressive (AR) model parameter estimation. AR model parameters are estimated according to the Yule-Walker equation for offline feature extraction, whereas the weighted least square estimation (WLSE) is adopted for efficient online AR model parameter estimation. We also consider principal component analysis (PCA) and linear discriminant analysis (LDA) for dimensionality reduction and the most discriminative information extraction to aid online epileptic seizure detection. The extracted information is imported to the online detector implanted on the brain surface. We consider PCA combined with cosine similarity and LDA-based similarity score as a decision rule. Simulation result shows that the PCA method combined with cosine similarity has average 96.6% detection accuracy and 1.2 ms detection latency for the test vectors under study. For fair comparison of detector performance, the line length detection algorithm is tested under the same simulation condition and the result shows that the approach based on the PCA method with cosine similarity measure outperforms the LDA-based approach and the line length detection algorithm. Based on the study, we further propose a hybrid approach combining PCA with cosine similarity and an LDA-based similarity score method. The simulation result shows that the hybrid method has a higher detection rate under the same false detection rate compared to the PCA method with cosine similarity measure. | - |
dc.language | 영어 | - |
dc.language.iso | en | - |
dc.publisher | AMER SCIENTIFIC PUBLISHERS | - |
dc.relation.isPartOf | JOURNAL OF MEDICAL IMAGING AND HEALTH INFORMATICS | - |
dc.subject | HIGH-FREQUENCY OSCILLATIONS | - |
dc.subject | STIMULATION | - |
dc.subject | BRAIN | - |
dc.title | Online Epileptic Seizure Detection: Statistical Signal Processing Based on Multichannel Electroencephalogram Sensors | - |
dc.type | Article | - |
dc.type.rims | ART | - |
dc.description.journalClass | 1 | - |
dc.identifier.wosid | 000423786700007 | - |
dc.identifier.doi | 10.1166/jmihi.2018.2317 | - |
dc.identifier.bibliographicCitation | JOURNAL OF MEDICAL IMAGING AND HEALTH INFORMATICS, v.8, no.2, pp.196 - 207 | - |
dc.citation.endPage | 207 | - |
dc.citation.startPage | 196 | - |
dc.citation.title | JOURNAL OF MEDICAL IMAGING AND HEALTH INFORMATICS | - |
dc.citation.volume | 8 | - |
dc.citation.number | 2 | - |
dc.contributor.affiliatedAuthor | Kim, Jungsuk | - |
dc.type.docType | Article | - |
dc.subject.keywordAuthor | EEG Sensor | - |
dc.subject.keywordAuthor | Epileptic Seizure | - |
dc.subject.keywordAuthor | Statistical Signal Processing | - |
dc.subject.keywordPlus | HIGH-FREQUENCY OSCILLATIONS | - |
dc.subject.keywordPlus | STIMULATION | - |
dc.subject.keywordPlus | BRAIN | - |
dc.relation.journalResearchArea | Mathematical & Computational Biology | - |
dc.relation.journalResearchArea | Radiology, Nuclear Medicine & Medical Imaging | - |
dc.relation.journalWebOfScienceCategory | Mathematical & Computational Biology | - |
dc.relation.journalWebOfScienceCategory | Radiology, Nuclear Medicine & Medical Imaging | - |
dc.description.journalRegisteredClass | scie | - |
Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.
1342, Seongnam-daero, Sujeong-gu, Seongnam-si, Gyeonggi-do, Republic of Korea(13120)031-750-5114
COPYRIGHT 2020 Gachon University All Rights Reserved.
Certain data included herein are derived from the © Web of Science of Clarivate Analytics. All rights reserved.
You may not copy or re-distribute this material in whole or in part without the prior written consent of Clarivate Analytics.