Cargando…

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...

Descripción completa

Detalles Bibliográficos
Autores principales: Li, Chunsheng, Liu, Shiyue, Wang, Zeyu, Yuan, Guanqian
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2023
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
_version_ 1784871948536250368
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
work_keys_str_mv AT lichunsheng classifyingepilepticphaseamplitudecouplinginseegusingcomplexvaluedconvolutionalneuralnetwork
AT liushiyue classifyingepilepticphaseamplitudecouplinginseegusingcomplexvaluedconvolutionalneuralnetwork
AT wangzeyu classifyingepilepticphaseamplitudecouplinginseegusingcomplexvaluedconvolutionalneuralnetwork
AT yuanguanqian classifyingepilepticphaseamplitudecouplinginseegusingcomplexvaluedconvolutionalneuralnetwork