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A multi-head self-attention deep learning approach for detection and recommendation of neuromagnetic high frequency oscillations in epilepsy
Magnetoencephalography is a noninvasive neuromagnetic technology to record epileptic activities for the pre-operative localization of epileptogenic zones, which has received increasing attention in the diagnosis and surgery of epilepsy. As reported by recent studies, pathological high frequency osci...
Autores principales: | , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9500293/ https://www.ncbi.nlm.nih.gov/pubmed/36156983 http://dx.doi.org/10.3389/fninf.2022.771965 |
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author | Zhao, Xiangyu Peng, Xueping Niu, Ke Li, Hailong He, Lili Yang, Feng Wu, Ting Chen, Duo Zhang, Qiusi Ouyang, Menglin Guo, Jiayang Pan, Yijie |
author_facet | Zhao, Xiangyu Peng, Xueping Niu, Ke Li, Hailong He, Lili Yang, Feng Wu, Ting Chen, Duo Zhang, Qiusi Ouyang, Menglin Guo, Jiayang Pan, Yijie |
author_sort | Zhao, Xiangyu |
collection | PubMed |
description | Magnetoencephalography is a noninvasive neuromagnetic technology to record epileptic activities for the pre-operative localization of epileptogenic zones, which has received increasing attention in the diagnosis and surgery of epilepsy. As reported by recent studies, pathological high frequency oscillations (HFOs), when utilized as a biomarker to localize the epileptogenic zones, result in a significant reduction in seizure frequency, even seizure elimination in around 80% of cases. Thus, objective, rapid, and automatic detection and recommendation of HFOs are highly desirable for clinicians to alleviate the burden of reviewing a large amount of MEG data from a given patient. Despite the advantage, the performance of existing HFOs rarely satisfies the clinical requirement. Consequently, no HFOs have been successfully applied to real clinical applications so far. In this work, we propose a multi-head self-attention-based detector for recommendation, termed MSADR, to detect and recommend HFO signals. Taking advantage of the state-of-the-art multi-head self-attention mechanism in deep learning, the proposed MSADR achieves a more superior accuracy of 88.6% than peer machine learning models in both detection and recommendation tasks. In addition, the robustness of MSADR is also extensively assessed with various ablation tests, results of which further demonstrate the effectiveness and generalizability of the proposed approach. |
format | Online Article Text |
id | pubmed-9500293 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-95002932022-09-24 A multi-head self-attention deep learning approach for detection and recommendation of neuromagnetic high frequency oscillations in epilepsy Zhao, Xiangyu Peng, Xueping Niu, Ke Li, Hailong He, Lili Yang, Feng Wu, Ting Chen, Duo Zhang, Qiusi Ouyang, Menglin Guo, Jiayang Pan, Yijie Front Neuroinform Neuroscience Magnetoencephalography is a noninvasive neuromagnetic technology to record epileptic activities for the pre-operative localization of epileptogenic zones, which has received increasing attention in the diagnosis and surgery of epilepsy. As reported by recent studies, pathological high frequency oscillations (HFOs), when utilized as a biomarker to localize the epileptogenic zones, result in a significant reduction in seizure frequency, even seizure elimination in around 80% of cases. Thus, objective, rapid, and automatic detection and recommendation of HFOs are highly desirable for clinicians to alleviate the burden of reviewing a large amount of MEG data from a given patient. Despite the advantage, the performance of existing HFOs rarely satisfies the clinical requirement. Consequently, no HFOs have been successfully applied to real clinical applications so far. In this work, we propose a multi-head self-attention-based detector for recommendation, termed MSADR, to detect and recommend HFO signals. Taking advantage of the state-of-the-art multi-head self-attention mechanism in deep learning, the proposed MSADR achieves a more superior accuracy of 88.6% than peer machine learning models in both detection and recommendation tasks. In addition, the robustness of MSADR is also extensively assessed with various ablation tests, results of which further demonstrate the effectiveness and generalizability of the proposed approach. Frontiers Media S.A. 2022-09-09 /pmc/articles/PMC9500293/ /pubmed/36156983 http://dx.doi.org/10.3389/fninf.2022.771965 Text en Copyright © 2022 Zhao, Peng, Niu, Li, He, Yang, Wu, Chen, Zhang, Ouyang, Guo and Pan. 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 | Neuroscience Zhao, Xiangyu Peng, Xueping Niu, Ke Li, Hailong He, Lili Yang, Feng Wu, Ting Chen, Duo Zhang, Qiusi Ouyang, Menglin Guo, Jiayang Pan, Yijie A multi-head self-attention deep learning approach for detection and recommendation of neuromagnetic high frequency oscillations in epilepsy |
title | A multi-head self-attention deep learning approach for detection and recommendation of neuromagnetic high frequency oscillations in epilepsy |
title_full | A multi-head self-attention deep learning approach for detection and recommendation of neuromagnetic high frequency oscillations in epilepsy |
title_fullStr | A multi-head self-attention deep learning approach for detection and recommendation of neuromagnetic high frequency oscillations in epilepsy |
title_full_unstemmed | A multi-head self-attention deep learning approach for detection and recommendation of neuromagnetic high frequency oscillations in epilepsy |
title_short | A multi-head self-attention deep learning approach for detection and recommendation of neuromagnetic high frequency oscillations in epilepsy |
title_sort | multi-head self-attention deep learning approach for detection and recommendation of neuromagnetic high frequency oscillations in epilepsy |
topic | Neuroscience |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9500293/ https://www.ncbi.nlm.nih.gov/pubmed/36156983 http://dx.doi.org/10.3389/fninf.2022.771965 |
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