A Novel MEGNet for Classification of High-Frequency Oscillations in Magnetoencephalography of Epileptic Patients
Epilepsy is a neurological disease, and the location of a lesion before neurosurgery or invasive intracranial electroencephalography (iEEG) surgery using intracranial electrodes is often very challenging. The high-frequency oscillation (HFOs) mode in MEG signal can now be used to detect lesions. Due...
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| Format: | Article |
| Language: | English |
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Wiley
2020-01-01
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| Series: | Complexity |
| Online Access: | http://dx.doi.org/10.1155/2020/9237808 |
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| author | Jun Liu Siqi Sun Yang Liu Jiayang Guo Hailong Li Yuan Gao Jintao Sun Jing Xiang |
| author_facet | Jun Liu Siqi Sun Yang Liu Jiayang Guo Hailong Li Yuan Gao Jintao Sun Jing Xiang |
| author_sort | Jun Liu |
| collection | DOAJ |
| description | Epilepsy is a neurological disease, and the location of a lesion before neurosurgery or invasive intracranial electroencephalography (iEEG) surgery using intracranial electrodes is often very challenging. The high-frequency oscillation (HFOs) mode in MEG signal can now be used to detect lesions. Due to the time-consuming and error-prone operation of HFOs detection, an automatic HFOs detector with high accuracy is very necessary in modern medicine. Therefore, an optimized capsule neural network was used, and a MEG (magnetoencephalograph) HFOs detector based on MEGNet was proposed to facilitate the clinical detection of HFOs. To the best of our knowledge, this is the first time that a neural network has been used to detect HFOs in MEG. After optimized configuration, the accuracy, precision, recall, and F1-score of the proposed detector reached 94%, 95%, 94%, and 94%, which were better than other classical machine learning models. In addition, we used the k-fold cross-validation scheme to test the performance consistency of the model. The distribution of various performance indicators shows that our model is robust. |
| format | Article |
| id | doaj-art-c23211c486f04b6f84295a923812d54e |
| institution | Kabale University |
| issn | 1076-2787 1099-0526 |
| language | English |
| publishDate | 2020-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Complexity |
| spelling | doaj-art-c23211c486f04b6f84295a923812d54e2025-02-03T05:52:28ZengWileyComplexity1076-27871099-05262020-01-01202010.1155/2020/92378089237808A Novel MEGNet for Classification of High-Frequency Oscillations in Magnetoencephalography of Epileptic PatientsJun Liu0Siqi Sun1Yang Liu2Jiayang Guo3Hailong Li4Yuan Gao5Jintao Sun6Jing Xiang7Hubei Key Laboratory of Intelligent Robot, Wuhan Institute of Technology, Wuhan 430205, ChinaSchool of Computer Science & Engineering, Wuhan Institute of Technology, Wuhan 430205, ChinaSchool of Computer Science & Engineering, Wuhan Institute of Technology, Wuhan 430205, ChinaDepartment of Electrical Engineering and Computer Science, University of Cincinnati, Cincinnati, OH, USADepartment of Pediatrics, Cincinnati Children’s Hospital Medical Center, Cincinnati, OH, USADepartment of Neurosurgery, Nanjing Brain Hospital, Nanjing, ChinaDepartment of Neurosurgery, Nanjing Brain Hospital, Nanjing, ChinaDepartment of Neurology, Cincinnati Children’s Hospital Medical Center, Cincinnati, OH, USAEpilepsy is a neurological disease, and the location of a lesion before neurosurgery or invasive intracranial electroencephalography (iEEG) surgery using intracranial electrodes is often very challenging. The high-frequency oscillation (HFOs) mode in MEG signal can now be used to detect lesions. Due to the time-consuming and error-prone operation of HFOs detection, an automatic HFOs detector with high accuracy is very necessary in modern medicine. Therefore, an optimized capsule neural network was used, and a MEG (magnetoencephalograph) HFOs detector based on MEGNet was proposed to facilitate the clinical detection of HFOs. To the best of our knowledge, this is the first time that a neural network has been used to detect HFOs in MEG. After optimized configuration, the accuracy, precision, recall, and F1-score of the proposed detector reached 94%, 95%, 94%, and 94%, which were better than other classical machine learning models. In addition, we used the k-fold cross-validation scheme to test the performance consistency of the model. The distribution of various performance indicators shows that our model is robust.http://dx.doi.org/10.1155/2020/9237808 |
| spellingShingle | Jun Liu Siqi Sun Yang Liu Jiayang Guo Hailong Li Yuan Gao Jintao Sun Jing Xiang A Novel MEGNet for Classification of High-Frequency Oscillations in Magnetoencephalography of Epileptic Patients Complexity |
| title | A Novel MEGNet for Classification of High-Frequency Oscillations in Magnetoencephalography of Epileptic Patients |
| title_full | A Novel MEGNet for Classification of High-Frequency Oscillations in Magnetoencephalography of Epileptic Patients |
| title_fullStr | A Novel MEGNet for Classification of High-Frequency Oscillations in Magnetoencephalography of Epileptic Patients |
| title_full_unstemmed | A Novel MEGNet for Classification of High-Frequency Oscillations in Magnetoencephalography of Epileptic Patients |
| title_short | A Novel MEGNet for Classification of High-Frequency Oscillations in Magnetoencephalography of Epileptic Patients |
| title_sort | novel megnet for classification of high frequency oscillations in magnetoencephalography of epileptic patients |
| url | http://dx.doi.org/10.1155/2020/9237808 |
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