Classifying epileptic phase-amplitude coupling in SEEG using complex-valued convolutional neural network
EEG phase-amplitude coupling (PAC), the amplitude of high-frequency oscillations modulated by the phase of low-frequency oscillations (LFOs), is a useful biomarker to localize epileptogenic tissue. It is commonly represented in a comodulogram of coupling strength but without coupled phase informatio...
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Frontiers Media S.A.
2023-01-01
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| Series: | Frontiers in Physiology |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fphys.2022.1085530/full |
| _version_ | 1797960827556331520 |
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| author | Chunsheng Li Shiyue Liu Zeyu Wang Zeyu Wang Guanqian Yuan |
| author_facet | Chunsheng Li Shiyue Liu Zeyu Wang Zeyu Wang Guanqian Yuan |
| author_sort | Chunsheng Li |
| collection | DOAJ |
| description | EEG phase-amplitude coupling (PAC), the amplitude of high-frequency oscillations modulated by the phase of low-frequency oscillations (LFOs), is a useful biomarker to localize epileptogenic tissue. It is commonly represented in a comodulogram of coupling strength but without coupled phase information. The phase-amplitude coupling is also found in the normal brain, and it is difficult to discriminate pathological phase-amplitude couplings from normal ones. This study proposes a novel approach based on complex-valued phase-amplitude coupling (CV-PAC) for classifying epileptic phase-amplitude coupling. The CV-PAC combines both the coupling strengths and the coupled phases of low-frequency oscillations. The complex-valued convolutional neural network (CV-CNN) is then used to classify epileptic CV-PAC. Stereo-electroencephalography (SEEG) recordings from nine intractable epilepsy patients were analyzed. The leave-one-out cross-validation is performed, and the area-under-curve (AUC) value is used as the indicator of the performance of different measures. Our result shows that the area-under-curve value is .92 for classifying epileptic CV-PAC using CV-CNN. The area-under-curve value decreases to .89, .80, and .88 while using traditional convolutional neural networks, support vector machine, and random forest, respectively. The phases of delta (1–4 Hz) and alpha (8–10 Hz) bands are different between epileptic and normal CV-PAC. The phase information of CV-PAC is important for improving classification performance. The proposed approach of CV-PAC/CV-CNN promises to identify more accurate epileptic brain activities for potential surgical intervention. |
| first_indexed | 2024-04-11T00:50:19Z |
| format | Article |
| id | doaj.art-a14fd09fb1194a8eb98c55eaa065ba30 |
| institution | Directory Open Access Journal |
| issn | 1664-042X |
| language | English |
| last_indexed | 2024-04-11T00:50:19Z |
| publishDate | 2023-01-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Physiology |
| spelling | doaj.art-a14fd09fb1194a8eb98c55eaa065ba302023-01-05T09:13:16ZengFrontiers Media S.A.Frontiers in Physiology1664-042X2023-01-011310.3389/fphys.2022.10855301085530Classifying epileptic phase-amplitude coupling in SEEG using complex-valued convolutional neural networkChunsheng Li0Shiyue Liu1Zeyu Wang2Zeyu Wang3Guanqian Yuan4Department of Biomedical Engineering, School of Electrical Engineering, Shenyang University of Technology, Shenyang, ChinaDepartment of Biomedical Engineering, School of Electrical Engineering, Shenyang University of Technology, Shenyang, ChinaDepartment of Biomedical Engineering, School of Electrical Engineering, Shenyang University of Technology, Shenyang, ChinaDepartment of Electrical Engineering and Information Systems, University of Pannonia, Veszprem, HungaryDepartment of Neurosurgery, General Hospital of Northern Theater Command, Shenyang, ChinaEEG phase-amplitude coupling (PAC), the amplitude of high-frequency oscillations modulated by the phase of low-frequency oscillations (LFOs), is a useful biomarker to localize epileptogenic tissue. It is commonly represented in a comodulogram of coupling strength but without coupled phase information. The phase-amplitude coupling is also found in the normal brain, and it is difficult to discriminate pathological phase-amplitude couplings from normal ones. This study proposes a novel approach based on complex-valued phase-amplitude coupling (CV-PAC) for classifying epileptic phase-amplitude coupling. The CV-PAC combines both the coupling strengths and the coupled phases of low-frequency oscillations. The complex-valued convolutional neural network (CV-CNN) is then used to classify epileptic CV-PAC. Stereo-electroencephalography (SEEG) recordings from nine intractable epilepsy patients were analyzed. The leave-one-out cross-validation is performed, and the area-under-curve (AUC) value is used as the indicator of the performance of different measures. Our result shows that the area-under-curve value is .92 for classifying epileptic CV-PAC using CV-CNN. The area-under-curve value decreases to .89, .80, and .88 while using traditional convolutional neural networks, support vector machine, and random forest, respectively. The phases of delta (1–4 Hz) and alpha (8–10 Hz) bands are different between epileptic and normal CV-PAC. The phase information of CV-PAC is important for improving classification performance. The proposed approach of CV-PAC/CV-CNN promises to identify more accurate epileptic brain activities for potential surgical intervention.https://www.frontiersin.org/articles/10.3389/fphys.2022.1085530/fullepilepsySEEGcomplex-valued phase-amplitude couplingcomplex-valued convolutional neural networkepileptogenic zone |
| spellingShingle | Chunsheng Li Shiyue Liu Zeyu Wang Zeyu Wang Guanqian Yuan Classifying epileptic phase-amplitude coupling in SEEG using complex-valued convolutional neural network Frontiers in Physiology epilepsy SEEG complex-valued phase-amplitude coupling complex-valued convolutional neural network epileptogenic zone |
| title | Classifying epileptic phase-amplitude coupling in SEEG using complex-valued convolutional neural network |
| title_full | Classifying epileptic phase-amplitude coupling in SEEG using complex-valued convolutional neural network |
| title_fullStr | Classifying epileptic phase-amplitude coupling in SEEG using complex-valued convolutional neural network |
| title_full_unstemmed | Classifying epileptic phase-amplitude coupling in SEEG using complex-valued convolutional neural network |
| title_short | Classifying epileptic phase-amplitude coupling in SEEG using complex-valued convolutional neural network |
| title_sort | classifying epileptic phase amplitude coupling in seeg using complex valued convolutional neural network |
| topic | epilepsy SEEG complex-valued phase-amplitude coupling complex-valued convolutional neural network epileptogenic zone |
| url | https://www.frontiersin.org/articles/10.3389/fphys.2022.1085530/full |
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