Intelligent Diagnosis and Research of Epileptic Diseases Based on EEG Signals
Aiming at the problem of low accuracy and classification of epileptic EEG in medical diagnosis,a signal classification and detection technique based on particle swarm optimization (PSO) was proposed to optimize the support vector machine (SVM) based on the theory of particle swarm optimization and s...
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| Format: | Article |
| Language: | zho |
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Harbin University of Science and Technology Publications
2018-06-01
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| Series: | Journal of Harbin University of Science and Technology |
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| Online Access: | https://hlgxb.hrbust.edu.cn/#/digest?ArticleID=1538 |
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| _version_ | 1849227998173069312 |
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| author | LIU Chang-yuan ZHANG Fu-hao WEI Qi |
| author_facet | LIU Chang-yuan ZHANG Fu-hao WEI Qi |
| author_sort | LIU Chang-yuan |
| collection | DOAJ |
| description | Aiming at the problem of low accuracy and classification of epileptic EEG in medical diagnosis,a signal classification and detection technique based on particle swarm optimization (PSO) was proposed to optimize the support vector machine (SVM) based on the theory of particle swarm optimization and support vector machine (SVM).Firstly, the EEG signals were decomposed and reconstructed by wavelet analysis.Secondly, the coefficients of fluctuation and approximate entropy of the reconstructed signals containing the functional parameters of epilepsy were extracted. Finally, The support vector machine (SVM) optimized by particle swarm optimization (PSO) is used to classify the EEG signals. The experimental results show that the this method can correctly identify three types of EEG signals in healthy, interictal epilepsy and epileptic seizures, the final recognition rate can reach 99.83%. |
| format | Article |
| id | doaj-art-2ead71bf6b1341f9b571d4b5e15840ba |
| institution | Kabale University |
| issn | 1007-2683 |
| language | zho |
| publishDate | 2018-06-01 |
| publisher | Harbin University of Science and Technology Publications |
| record_format | Article |
| series | Journal of Harbin University of Science and Technology |
| spelling | doaj-art-2ead71bf6b1341f9b571d4b5e15840ba2025-08-23T07:41:43ZzhoHarbin University of Science and Technology PublicationsJournal of Harbin University of Science and Technology1007-26832018-06-012303919810.15938/j.jhust.2018.03.016Intelligent Diagnosis and Research of Epileptic Diseases Based on EEG SignalsLIU Chang-yuan0ZHANG Fu-hao1WEI Qi2School of Electrical and Electronic Engineering， Harbin University of Science and Technology， Harbin 150080，ChinaSchool of Electrical and Electronic Engineering， Harbin University of Science and Technology， Harbin 150080，ChinaSchool of Electrical and Electronic Engineering， Harbin University of Science and Technology， Harbin 150080，ChinaAiming at the problem of low accuracy and classification of epileptic EEG in medical diagnosis,a signal classification and detection technique based on particle swarm optimization (PSO) was proposed to optimize the support vector machine (SVM) based on the theory of particle swarm optimization and support vector machine (SVM).Firstly, the EEG signals were decomposed and reconstructed by wavelet analysis.Secondly, the coefficients of fluctuation and approximate entropy of the reconstructed signals containing the functional parameters of epilepsy were extracted. Finally, The support vector machine (SVM) optimized by particle swarm optimization (PSO) is used to classify the EEG signals. The experimental results show that the this method can correctly identify three types of EEG signals in healthy, interictal epilepsy and epileptic seizures, the final recognition rate can reach 99.83%.https://hlgxb.hrbust.edu.cn/#/digest?ArticleID=1538epileptic eeg signalscoefficient of fluctuationapproximate entropyparticle swarm optimizationsupport vector machine |
| spellingShingle | LIU Chang-yuan ZHANG Fu-hao WEI Qi Intelligent Diagnosis and Research of Epileptic Diseases Based on EEG Signals Journal of Harbin University of Science and Technology epileptic eeg signals coefficient of fluctuation approximate entropy particle swarm optimization support vector machine |
| title | Intelligent Diagnosis and Research of Epileptic Diseases Based on EEG Signals |
| title_full | Intelligent Diagnosis and Research of Epileptic Diseases Based on EEG Signals |
| title_fullStr | Intelligent Diagnosis and Research of Epileptic Diseases Based on EEG Signals |
| title_full_unstemmed | Intelligent Diagnosis and Research of Epileptic Diseases Based on EEG Signals |
| title_short | Intelligent Diagnosis and Research of Epileptic Diseases Based on EEG Signals |
| title_sort | intelligent diagnosis and research of epileptic diseases based on eeg signals |
| topic | epileptic eeg signals coefficient of fluctuation approximate entropy particle swarm optimization support vector machine |
| url | https://hlgxb.hrbust.edu.cn/#/digest?ArticleID=1538 |
| work_keys_str_mv | AT liuchangyuan intelligentdiagnosisandresearchofepilepticdiseasesbasedoneegsignals AT zhangfuhao intelligentdiagnosisandresearchofepilepticdiseasesbasedoneegsignals AT weiqi intelligentdiagnosisandresearchofepilepticdiseasesbasedoneegsignals |