A particle swarm algorithm optimization‐based SVM–KNN algorithm for epileptic EEG recognition
Epilepsy is a disease caused by abnormal discharges in the central nervous system. Automatic detection and accurate identification of epileptic seizures based on electroencephalography (EEG) are significant in the clinical diagnosis and treatment of epilepsy. In this paper, we first decompose the pa...
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| Published in | International journal of intelligent systems Vol. 37; no. 12; pp. 11233 - 11249 |
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| Main Authors | , , , , , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
New York
John Wiley & Sons, Inc
01.12.2022
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| Subjects | |
| Online Access | Get full text |
| ISSN | 0884-8173 1098-111X |
| DOI | 10.1002/int.23040 |
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| Abstract | Epilepsy is a disease caused by abnormal discharges in the central nervous system. Automatic detection and accurate identification of epileptic seizures based on electroencephalography (EEG) are significant in the clinical diagnosis and treatment of epilepsy. In this paper, we first decompose the patient's EEG signal into multiple intrinsic modal functions (IMFs) using empirical modal decomposition, then compute the mean, standard deviation, fluctuation index, and sample entropy of IMF1, and finally classify them using a fusion algorithm of support vector machine and K‐nearest neighbor optimized by particle swarm algorithm. The results of validation using the epileptic EEG data set from Bonn University show that the auto‐detection and fast recognition method proposed in this paper can achieve a high seizure accuracy recognition rate (≥95%) with only a small number of training samples, which has a good clinical application value. |
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| AbstractList | Epilepsy is a disease caused by abnormal discharges in the central nervous system. Automatic detection and accurate identification of epileptic seizures based on electroencephalography (EEG) are significant in the clinical diagnosis and treatment of epilepsy. In this paper, we first decompose the patient's EEG signal into multiple intrinsic modal functions (IMFs) using empirical modal decomposition, then compute the mean, standard deviation, fluctuation index, and sample entropy of IMF1, and finally classify them using a fusion algorithm of support vector machine and K‐nearest neighbor optimized by particle swarm algorithm. The results of validation using the epileptic EEG data set from Bonn University show that the auto‐detection and fast recognition method proposed in this paper can achieve a high seizure accuracy recognition rate (≥95%) with only a small number of training samples, which has a good clinical application value. |
| Author | Zhu, Jia Wang, Xiaoying Ling, Xiang Li, Zhicheng Dai, Min Hu, Kunpeng Ling, Yu Yang, Qintai Du, Yuxiao Li, Xianghuan |
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| Cites_doi | 10.1016/S1474-4422(02)00003-0 10.1016/j.clinph.2009.09.002 10.1109/51.956815 10.1109/ICASSP.2019.8683229 10.1109/IEMENTECH.2017.8076992 10.1109/TNSRE.2016.2552539 10.1016/j.psychres.2011.06.020 10.1177/107385849600200213 10.1109/ICDSP.2017.8096036 10.1016/j.compbiomed.2017.09.017 10.1016/S0013-4694(97)00003-9 10.1109/TEVC.2010.2053935 10.1109/IEMBS.1997.756576 10.1109/LSP.2007.904710 10.1109/SPACES.2018.8316340 10.1109/JBHI.2012.2237409 10.1109/GCIS.2010.278 10.1109/BIBM.2016.7822562 10.1109/ICSPC.2007.4728632 10.1109/ICSESS.2014.6933697 10.1111/j.1528-1157.1982.tb05052.x 10.1109/CISP-BMEI.2016.7852954 10.1007/s11071-011-0281-2 10.1109/ISCO.2015.7282340 10.1109/ASSP.1989.28057 10.1016/j.amc.2006.09.022 10.1109/72.991427 10.1016/j.neucom.2017.03.027 10.1109/TBME.2007.905490 10.1109/INDCON.2011.6139341 10.2174/157340561001140424143814 10.1111/j.1528-1157.1998.tb01430.x 10.1007/BF00994018 10.1109/TIT.1967.1053964 |
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| Snippet | Epilepsy is a disease caused by abnormal discharges in the central nervous system. Automatic detection and accurate identification of epileptic seizures based... |
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| SubjectTerms | Algorithms Central nervous system Decomposition Electroencephalography empirical modal decomposition Epilepsy epilepsy EEG recognition Intelligent systems K‐nearest neighbor Optimization particle swarm optimization algorithm Recognition Seizures support vector machine Support vector machines |
| Title | A particle swarm algorithm optimization‐based SVM–KNN algorithm for epileptic EEG recognition |
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