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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| Main Authors: | , , |
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| Format: | Article |
| Language: | zho |
| Published: |
Harbin University of Science and Technology Publications
2018-06-01
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| Series: | Journal of Harbin University of Science and Technology |
| Subjects: | |
| Online Access: | https://hlgxb.hrbust.edu.cn/#/digest?ArticleID=1538 |
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| Summary: | 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%. |
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| ISSN: | 1007-2683 |