Detecting Epileptic Seizures in EEG Signals with Complementary Ensemble Empirical Mode Decomposition and Extreme Gradient Boosting
Epilepsy is a common nervous system disease that is characterized by recurrent seizures. An electroencephalogram (EEG) records neural activity, and it is commonly used for the diagnosis of epilepsy. To achieve accurate detection of epileptic seizures, an automatic detection approach of epileptic sei...
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MDPI AG
2020-01-01
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| Online Access: | https://www.mdpi.com/1099-4300/22/2/140 |
| _version_ | 1798038156416647168 |
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| author | Jiang Wu Tengfei Zhou Taiyong Li |
| author_facet | Jiang Wu Tengfei Zhou Taiyong Li |
| author_sort | Jiang Wu |
| collection | DOAJ |
| description | Epilepsy is a common nervous system disease that is characterized by recurrent seizures. An electroencephalogram (EEG) records neural activity, and it is commonly used for the diagnosis of epilepsy. To achieve accurate detection of epileptic seizures, an automatic detection approach of epileptic seizures, integrating complementary ensemble empirical mode decomposition (CEEMD) and extreme gradient boosting (XGBoost), named CEEMD-XGBoost, is proposed. Firstly, the decomposition method, CEEMD, which is capable of effectively reducing the influence of mode mixing and end effects, was utilized to divide raw EEG signals into a set of intrinsic mode functions (<i>IMF</i>s) and residues. Secondly, the multi-domain features were extracted from raw signals and the decomposed components, and they were further selected according to the importance scores of the extracted features. Finally, XGBoost was applied to develop the epileptic seizure detection model. Experiments were conducted on two benchmark epilepsy EEG datasets, named the Bonn dataset and the CHB-MIT (Children’s Hospital Boston and Massachusetts Institute of Technology) dataset, to evaluate the performance of our proposed CEEMD-XGBoost. The extensive experimental results indicated that, compared with some previous EEG classification models, CEEMD-XGBoost can significantly enhance the detection performance of epileptic seizures in terms of sensitivity, specificity, and accuracy. |
| first_indexed | 2024-04-11T21:36:22Z |
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| institution | Directory Open Access Journal |
| issn | 1099-4300 |
| language | English |
| last_indexed | 2024-04-11T21:36:22Z |
| publishDate | 2020-01-01 |
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| spelling | doaj.art-33e5fd1b261e4dd683afc76fccf40ead2022-12-22T04:01:43ZengMDPI AGEntropy1099-43002020-01-0122214010.3390/e22020140e22020140Detecting Epileptic Seizures in EEG Signals with Complementary Ensemble Empirical Mode Decomposition and Extreme Gradient BoostingJiang Wu0Tengfei Zhou1Taiyong Li2School of Economic Information Engineering, Southwestern University of Finance and Economics, Chengdu 611130, ChinaSchool of Economic Information Engineering, Southwestern University of Finance and Economics, Chengdu 611130, ChinaSchool of Economic Information Engineering, Southwestern University of Finance and Economics, Chengdu 611130, ChinaEpilepsy is a common nervous system disease that is characterized by recurrent seizures. An electroencephalogram (EEG) records neural activity, and it is commonly used for the diagnosis of epilepsy. To achieve accurate detection of epileptic seizures, an automatic detection approach of epileptic seizures, integrating complementary ensemble empirical mode decomposition (CEEMD) and extreme gradient boosting (XGBoost), named CEEMD-XGBoost, is proposed. Firstly, the decomposition method, CEEMD, which is capable of effectively reducing the influence of mode mixing and end effects, was utilized to divide raw EEG signals into a set of intrinsic mode functions (<i>IMF</i>s) and residues. Secondly, the multi-domain features were extracted from raw signals and the decomposed components, and they were further selected according to the importance scores of the extracted features. Finally, XGBoost was applied to develop the epileptic seizure detection model. Experiments were conducted on two benchmark epilepsy EEG datasets, named the Bonn dataset and the CHB-MIT (Children’s Hospital Boston and Massachusetts Institute of Technology) dataset, to evaluate the performance of our proposed CEEMD-XGBoost. The extensive experimental results indicated that, compared with some previous EEG classification models, CEEMD-XGBoost can significantly enhance the detection performance of epileptic seizures in terms of sensitivity, specificity, and accuracy.https://www.mdpi.com/1099-4300/22/2/140electroencephalogram (eeg)epileptic seizure detectioncomplementary ensemble empirical mode decomposition (ceemd)feature selectionextreme gradient boosting (xgboost) |
| spellingShingle | Jiang Wu Tengfei Zhou Taiyong Li Detecting Epileptic Seizures in EEG Signals with Complementary Ensemble Empirical Mode Decomposition and Extreme Gradient Boosting Entropy electroencephalogram (eeg) epileptic seizure detection complementary ensemble empirical mode decomposition (ceemd) feature selection extreme gradient boosting (xgboost) |
| title | Detecting Epileptic Seizures in EEG Signals with Complementary Ensemble Empirical Mode Decomposition and Extreme Gradient Boosting |
| title_full | Detecting Epileptic Seizures in EEG Signals with Complementary Ensemble Empirical Mode Decomposition and Extreme Gradient Boosting |
| title_fullStr | Detecting Epileptic Seizures in EEG Signals with Complementary Ensemble Empirical Mode Decomposition and Extreme Gradient Boosting |
| title_full_unstemmed | Detecting Epileptic Seizures in EEG Signals with Complementary Ensemble Empirical Mode Decomposition and Extreme Gradient Boosting |
| title_short | Detecting Epileptic Seizures in EEG Signals with Complementary Ensemble Empirical Mode Decomposition and Extreme Gradient Boosting |
| title_sort | detecting epileptic seizures in eeg signals with complementary ensemble empirical mode decomposition and extreme gradient boosting |
| topic | electroencephalogram (eeg) epileptic seizure detection complementary ensemble empirical mode decomposition (ceemd) feature selection extreme gradient boosting (xgboost) |
| url | https://www.mdpi.com/1099-4300/22/2/140 |
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