Deep extreme learning machine with knowledge augmentation for EEG seizure signal recognition
IntroductionIntelligent recognition of electroencephalogram (EEG) signals can remarkably improve the accuracy of epileptic seizure prediction, which is essential for epileptic diagnosis. Extreme learning machine (ELM) has been applied to EEG signals recognition, however, the artifacts and noises in...
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Frontiers Media S.A.
2023-08-01
|
| Series: | Frontiers in Neuroinformatics |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fninf.2023.1205529/full |
| _version_ | 1797737607365394432 |
|---|---|
| author | Xiongtao Zhang Xiongtao Zhang Shuai Dong Shuai Dong Qing Shen Qing Shen Jie Zhou Jingjing Min |
| author_facet | Xiongtao Zhang Xiongtao Zhang Shuai Dong Shuai Dong Qing Shen Qing Shen Jie Zhou Jingjing Min |
| author_sort | Xiongtao Zhang |
| collection | DOAJ |
| description | IntroductionIntelligent recognition of electroencephalogram (EEG) signals can remarkably improve the accuracy of epileptic seizure prediction, which is essential for epileptic diagnosis. Extreme learning machine (ELM) has been applied to EEG signals recognition, however, the artifacts and noises in EEG signals have a serious effect on recognition efficiency. Deep learning is capable of noise resistance, contributing to removing the noise in raw EEG signals. But traditional deep networks suffer from time-consuming training and slow convergence.MethodsTherefore, a novel deep learning based ELM (denoted as DELM) motivated by stacking generalization principle is proposed in this paper. Deep extreme learning machine (DELM) is a hierarchical network composed of several independent ELM modules. Augmented EEG knowledge is taken as complementary component, which will then be mapped into next module. This learning process is so simple and fast, meanwhile, it can excavate the implicit knowledge in raw data to a greater extent. Additionally, the proposed method is operated in a single-direction manner, so there is no need to perform parameters fine-tuning, which saves the expense of time.ResultsExtensive experiments are conducted on the public Bonn EEG dataset. The experimental results demonstrate that compared with the commonly-used seizure prediction methods, the proposed DELM wins the best average accuracies in 13 out of the 22 data and the best average F-measure scores in 10 out of the 22 data. And the running time of DELM is more than two times quickly than deep learning methods.DiscussionTherefore, DELM is superior to traditional and some state-of-the-art machine learning methods. The proposed architecture demonstrates its feasibility and superiority in epileptic EEG signal recognition. The proposed less computationally intensive deep classifier enables faster seizure onset detection, which is showing great potential on the application of real-time EEG signal classification. |
| first_indexed | 2024-03-12T13:30:51Z |
| format | Article |
| id | doaj.art-d38570a7d5b34fdf909214b7038346d3 |
| institution | Directory Open Access Journal |
| issn | 1662-5196 |
| language | English |
| last_indexed | 2024-03-12T13:30:51Z |
| publishDate | 2023-08-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Neuroinformatics |
| spelling | doaj.art-d38570a7d5b34fdf909214b7038346d32023-08-24T13:45:41ZengFrontiers Media S.A.Frontiers in Neuroinformatics1662-51962023-08-011710.3389/fninf.2023.12055291205529Deep extreme learning machine with knowledge augmentation for EEG seizure signal recognitionXiongtao Zhang0Xiongtao Zhang1Shuai Dong2Shuai Dong3Qing Shen4Qing Shen5Jie Zhou6Jingjing Min7School of Information Engineering, Huzhou University, Huzhou, ChinaZhejiang Province Key Laboratory of Smart Management and Application of Modern Agricultural Resources, Huzhou University, Huzhou, ChinaSchool of Information Engineering, Huzhou University, Huzhou, ChinaZhejiang Province Key Laboratory of Smart Management and Application of Modern Agricultural Resources, Huzhou University, Huzhou, ChinaSchool of Information Engineering, Huzhou University, Huzhou, ChinaZhejiang Province Key Laboratory of Smart Management and Application of Modern Agricultural Resources, Huzhou University, Huzhou, ChinaDepartment of Computer Science and Engineering, Shaoxing University, Shaoxing, ChinaDepartment of Neurology, The First People's Hospital of Huzhou, First Affiliated Hospital of Huzhou University, Huzhou, ChinaIntroductionIntelligent recognition of electroencephalogram (EEG) signals can remarkably improve the accuracy of epileptic seizure prediction, which is essential for epileptic diagnosis. Extreme learning machine (ELM) has been applied to EEG signals recognition, however, the artifacts and noises in EEG signals have a serious effect on recognition efficiency. Deep learning is capable of noise resistance, contributing to removing the noise in raw EEG signals. But traditional deep networks suffer from time-consuming training and slow convergence.MethodsTherefore, a novel deep learning based ELM (denoted as DELM) motivated by stacking generalization principle is proposed in this paper. Deep extreme learning machine (DELM) is a hierarchical network composed of several independent ELM modules. Augmented EEG knowledge is taken as complementary component, which will then be mapped into next module. This learning process is so simple and fast, meanwhile, it can excavate the implicit knowledge in raw data to a greater extent. Additionally, the proposed method is operated in a single-direction manner, so there is no need to perform parameters fine-tuning, which saves the expense of time.ResultsExtensive experiments are conducted on the public Bonn EEG dataset. The experimental results demonstrate that compared with the commonly-used seizure prediction methods, the proposed DELM wins the best average accuracies in 13 out of the 22 data and the best average F-measure scores in 10 out of the 22 data. And the running time of DELM is more than two times quickly than deep learning methods.DiscussionTherefore, DELM is superior to traditional and some state-of-the-art machine learning methods. The proposed architecture demonstrates its feasibility and superiority in epileptic EEG signal recognition. The proposed less computationally intensive deep classifier enables faster seizure onset detection, which is showing great potential on the application of real-time EEG signal classification.https://www.frontiersin.org/articles/10.3389/fninf.2023.1205529/fullmultilayer extreme learning machinedeep networkknowledge utilizationEEGseizure recognition |
| spellingShingle | Xiongtao Zhang Xiongtao Zhang Shuai Dong Shuai Dong Qing Shen Qing Shen Jie Zhou Jingjing Min Deep extreme learning machine with knowledge augmentation for EEG seizure signal recognition Frontiers in Neuroinformatics multilayer extreme learning machine deep network knowledge utilization EEG seizure recognition |
| title | Deep extreme learning machine with knowledge augmentation for EEG seizure signal recognition |
| title_full | Deep extreme learning machine with knowledge augmentation for EEG seizure signal recognition |
| title_fullStr | Deep extreme learning machine with knowledge augmentation for EEG seizure signal recognition |
| title_full_unstemmed | Deep extreme learning machine with knowledge augmentation for EEG seizure signal recognition |
| title_short | Deep extreme learning machine with knowledge augmentation for EEG seizure signal recognition |
| title_sort | deep extreme learning machine with knowledge augmentation for eeg seizure signal recognition |
| topic | multilayer extreme learning machine deep network knowledge utilization EEG seizure recognition |
| url | https://www.frontiersin.org/articles/10.3389/fninf.2023.1205529/full |
| work_keys_str_mv | AT xiongtaozhang deepextremelearningmachinewithknowledgeaugmentationforeegseizuresignalrecognition AT xiongtaozhang deepextremelearningmachinewithknowledgeaugmentationforeegseizuresignalrecognition AT shuaidong deepextremelearningmachinewithknowledgeaugmentationforeegseizuresignalrecognition AT shuaidong deepextremelearningmachinewithknowledgeaugmentationforeegseizuresignalrecognition AT qingshen deepextremelearningmachinewithknowledgeaugmentationforeegseizuresignalrecognition AT qingshen deepextremelearningmachinewithknowledgeaugmentationforeegseizuresignalrecognition AT jiezhou deepextremelearningmachinewithknowledgeaugmentationforeegseizuresignalrecognition AT jingjingmin deepextremelearningmachinewithknowledgeaugmentationforeegseizuresignalrecognition |