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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...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9849379/ https://www.ncbi.nlm.nih.gov/pubmed/36685186 http://dx.doi.org/10.3389/fphys.2022.1085530 |
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author | Li, Chunsheng Liu, Shiyue Wang, Zeyu Yuan, Guanqian |
author_facet | Li, Chunsheng Liu, Shiyue Wang, Zeyu Yuan, Guanqian |
author_sort | Li, Chunsheng |
collection | PubMed |
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. |
format | Online Article Text |
id | pubmed-9849379 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-98493792023-01-20 Classifying epileptic phase-amplitude coupling in SEEG using complex-valued convolutional neural network Li, Chunsheng Liu, Shiyue Wang, Zeyu Yuan, Guanqian Front Physiol Physiology 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. Frontiers Media S.A. 2023-01-05 /pmc/articles/PMC9849379/ /pubmed/36685186 http://dx.doi.org/10.3389/fphys.2022.1085530 Text en Copyright © 2023 Li, Liu, Wang and Yuan. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Physiology Li, Chunsheng Liu, Shiyue Wang, Zeyu Yuan, Guanqian Classifying epileptic phase-amplitude coupling in SEEG using complex-valued convolutional neural network |
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 | Physiology |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9849379/ https://www.ncbi.nlm.nih.gov/pubmed/36685186 http://dx.doi.org/10.3389/fphys.2022.1085530 |
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