EEG-based real-time diagnostic system with developed dynamic 2TEMD and dynamic ApEn algorithms
In real-time electroencephalography (EEG) analysis, the problem of observing dynamic changes and the problem of binary classification is a promising direction. EEG energy and complexity are important evaluation metrics in brain death determination in the field of EEG analysis. We developed two algor...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2023-05-01
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| Series: | Frontiers in Physiology |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fphys.2023.1165450/full |
| _version_ | 1797828880300507136 |
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| author | Ran Zhang Linfeng Sui Linfeng Sui Jinming Gong Jianting Cao Jianting Cao |
| author_facet | Ran Zhang Linfeng Sui Linfeng Sui Jinming Gong Jianting Cao Jianting Cao |
| author_sort | Ran Zhang |
| collection | DOAJ |
| description | In real-time electroencephalography (EEG) analysis, the problem of observing dynamic changes and the problem of binary classification is a promising direction. EEG energy and complexity are important evaluation metrics in brain death determination in the field of EEG analysis. We developed two algorithms, dynamic turning tangent empirical mode decomposition to compute EEG energy and dynamic approximate entropy to compute EEG complexity for brain death determination. The developed algorithm is applied to analyze 50 EEG data of coma patients and 50 EEG data of brain death patients. The validity of the dynamic analysis is confirmed by the accuracy rate derived from the comparison with turning tangent empirical mode decomposition and approximate entropy algorithms. We evaluated the EEG data of three patients using the built diagnostic system. The experimental results visually showed that the EEG energy ratio was higher in a coma state than that in brain death, while the complexity was lower than that in brain death. |
| first_indexed | 2024-04-09T13:11:28Z |
| format | Article |
| id | doaj.art-9dc6d72beb174997b4aa4fc58458e97a |
| institution | Directory Open Access Journal |
| issn | 1664-042X |
| language | English |
| last_indexed | 2024-04-09T13:11:28Z |
| publishDate | 2023-05-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Physiology |
| spelling | doaj.art-9dc6d72beb174997b4aa4fc58458e97a2023-05-12T07:03:49ZengFrontiers Media S.A.Frontiers in Physiology1664-042X2023-05-011410.3389/fphys.2023.11654501165450EEG-based real-time diagnostic system with developed dynamic 2TEMD and dynamic ApEn algorithmsRan Zhang0Linfeng Sui1Linfeng Sui2Jinming Gong3Jianting Cao4Jianting Cao5Saitama Institute of Technology, Saitama, JapanSaitama Institute of Technology, Saitama, JapanRIKEN Center for Advanced Intelligence Project (AIP), Tokyo, JapanSaitama Institute of Technology, Saitama, JapanSaitama Institute of Technology, Saitama, JapanRIKEN Center for Advanced Intelligence Project (AIP), Tokyo, JapanIn real-time electroencephalography (EEG) analysis, the problem of observing dynamic changes and the problem of binary classification is a promising direction. EEG energy and complexity are important evaluation metrics in brain death determination in the field of EEG analysis. We developed two algorithms, dynamic turning tangent empirical mode decomposition to compute EEG energy and dynamic approximate entropy to compute EEG complexity for brain death determination. The developed algorithm is applied to analyze 50 EEG data of coma patients and 50 EEG data of brain death patients. The validity of the dynamic analysis is confirmed by the accuracy rate derived from the comparison with turning tangent empirical mode decomposition and approximate entropy algorithms. We evaluated the EEG data of three patients using the built diagnostic system. The experimental results visually showed that the EEG energy ratio was higher in a coma state than that in brain death, while the complexity was lower than that in brain death.https://www.frontiersin.org/articles/10.3389/fphys.2023.1165450/fullEEG data analysisdynamic2TEMDApEnreal timediagnostic system |
| spellingShingle | Ran Zhang Linfeng Sui Linfeng Sui Jinming Gong Jianting Cao Jianting Cao EEG-based real-time diagnostic system with developed dynamic 2TEMD and dynamic ApEn algorithms Frontiers in Physiology EEG data analysis dynamic 2TEMD ApEn real time diagnostic system |
| title | EEG-based real-time diagnostic system with developed dynamic 2TEMD and dynamic ApEn algorithms |
| title_full | EEG-based real-time diagnostic system with developed dynamic 2TEMD and dynamic ApEn algorithms |
| title_fullStr | EEG-based real-time diagnostic system with developed dynamic 2TEMD and dynamic ApEn algorithms |
| title_full_unstemmed | EEG-based real-time diagnostic system with developed dynamic 2TEMD and dynamic ApEn algorithms |
| title_short | EEG-based real-time diagnostic system with developed dynamic 2TEMD and dynamic ApEn algorithms |
| title_sort | eeg based real time diagnostic system with developed dynamic 2temd and dynamic apen algorithms |
| topic | EEG data analysis dynamic 2TEMD ApEn real time diagnostic system |
| url | https://www.frontiersin.org/articles/10.3389/fphys.2023.1165450/full |
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