Epileptic EEG Signal Detection Using Variational Modal Decomposition and Improved Grey Wolf Algorithm
Epilepsy does great harm to the human body, and even threatens human life when it is serious. Therefore, research focused on the diagnosis and treatment of epilepsy holds paramount clinical significance. In this paper, we utilized variational modal decomposition (VMD) and an enhanced grey wolf algor...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-09-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/23/19/8078 |
| _version_ | 1797575157313699840 |
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| author | Yongxin Sun Xiaojuan Chen |
| author_facet | Yongxin Sun Xiaojuan Chen |
| author_sort | Yongxin Sun |
| collection | DOAJ |
| description | Epilepsy does great harm to the human body, and even threatens human life when it is serious. Therefore, research focused on the diagnosis and treatment of epilepsy holds paramount clinical significance. In this paper, we utilized variational modal decomposition (VMD) and an enhanced grey wolf algorithm to detect epileptic electroencephalogram (EEG) signals. Data were extracted from each patient’s preseizure period and seizure period of 200 s each, with every 2 s as a segment, meaning 100 data points could be obtained for each patient’s health period as well as 100 data points for each patient’s epilepsy period. Variational modal decomposition (VMD) was used to obtain the corresponding intrinsic modal function (VMF) of the data. Then, the differential entropy (DE) and high frequency detection (HFD) of each VMF were extracted as features. The improved grey wolf algorithm is adopted for a selected channel to improve the maximum value of the channel. Finally, the EEG signal samples were classified using a support vector machine (SVM) classifier to achieve the accurate detection of epilepsy EEG signals. Experimental results show that the accuracy, sensitivity and specificity of the proposed method can reach 98.3%, 98.9% and 98.5%, respectively. The proposed algorithm in this paper can be used as an index to detect epileptic seizures and has certain guiding significance for the early diagnosis and effective treatment of epileptic patients. |
| first_indexed | 2024-03-10T21:35:44Z |
| format | Article |
| id | doaj.art-75d2c1dfec354381992c2627cd385f4f |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-10T21:35:44Z |
| publishDate | 2023-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-75d2c1dfec354381992c2627cd385f4f2023-11-19T15:02:25ZengMDPI AGSensors1424-82202023-09-012319807810.3390/s23198078Epileptic EEG Signal Detection Using Variational Modal Decomposition and Improved Grey Wolf AlgorithmYongxin Sun0Xiaojuan Chen1College of Electronic Information Engineering, Changchun University of Science and Technology, Changchun 130022, ChinaCollege of Electronic Information Engineering, Changchun University of Science and Technology, Changchun 130022, ChinaEpilepsy does great harm to the human body, and even threatens human life when it is serious. Therefore, research focused on the diagnosis and treatment of epilepsy holds paramount clinical significance. In this paper, we utilized variational modal decomposition (VMD) and an enhanced grey wolf algorithm to detect epileptic electroencephalogram (EEG) signals. Data were extracted from each patient’s preseizure period and seizure period of 200 s each, with every 2 s as a segment, meaning 100 data points could be obtained for each patient’s health period as well as 100 data points for each patient’s epilepsy period. Variational modal decomposition (VMD) was used to obtain the corresponding intrinsic modal function (VMF) of the data. Then, the differential entropy (DE) and high frequency detection (HFD) of each VMF were extracted as features. The improved grey wolf algorithm is adopted for a selected channel to improve the maximum value of the channel. Finally, the EEG signal samples were classified using a support vector machine (SVM) classifier to achieve the accurate detection of epilepsy EEG signals. Experimental results show that the accuracy, sensitivity and specificity of the proposed method can reach 98.3%, 98.9% and 98.5%, respectively. The proposed algorithm in this paper can be used as an index to detect epileptic seizures and has certain guiding significance for the early diagnosis and effective treatment of epileptic patients.https://www.mdpi.com/1424-8220/23/19/8078epilepsyimproved grey wolf algorithmVMD |
| spellingShingle | Yongxin Sun Xiaojuan Chen Epileptic EEG Signal Detection Using Variational Modal Decomposition and Improved Grey Wolf Algorithm Sensors epilepsy improved grey wolf algorithm VMD |
| title | Epileptic EEG Signal Detection Using Variational Modal Decomposition and Improved Grey Wolf Algorithm |
| title_full | Epileptic EEG Signal Detection Using Variational Modal Decomposition and Improved Grey Wolf Algorithm |
| title_fullStr | Epileptic EEG Signal Detection Using Variational Modal Decomposition and Improved Grey Wolf Algorithm |
| title_full_unstemmed | Epileptic EEG Signal Detection Using Variational Modal Decomposition and Improved Grey Wolf Algorithm |
| title_short | Epileptic EEG Signal Detection Using Variational Modal Decomposition and Improved Grey Wolf Algorithm |
| title_sort | epileptic eeg signal detection using variational modal decomposition and improved grey wolf algorithm |
| topic | epilepsy improved grey wolf algorithm VMD |
| url | https://www.mdpi.com/1424-8220/23/19/8078 |
| work_keys_str_mv | AT yongxinsun epilepticeegsignaldetectionusingvariationalmodaldecompositionandimprovedgreywolfalgorithm AT xiaojuanchen epilepticeegsignaldetectionusingvariationalmodaldecompositionandimprovedgreywolfalgorithm |