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Weak supervision as an efficient approach for automated seizure detection in electroencephalography

Automated seizure detection from electroencephalography (EEG) would improve the quality of patient care while reducing medical costs, but achieving reliably high performance across patients has proven difficult. Convolutional Neural Networks (CNNs) show promise in addressing this problem, but they a...

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Autores principales: Saab, Khaled, Dunnmon, Jared, Ré, Christopher, Rubin, Daniel, Lee-Messer, Christopher
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7170880/
https://www.ncbi.nlm.nih.gov/pubmed/32352037
http://dx.doi.org/10.1038/s41746-020-0264-0
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author Saab, Khaled
Dunnmon, Jared
Ré, Christopher
Rubin, Daniel
Lee-Messer, Christopher
author_facet Saab, Khaled
Dunnmon, Jared
Ré, Christopher
Rubin, Daniel
Lee-Messer, Christopher
author_sort Saab, Khaled
collection PubMed
description Automated seizure detection from electroencephalography (EEG) would improve the quality of patient care while reducing medical costs, but achieving reliably high performance across patients has proven difficult. Convolutional Neural Networks (CNNs) show promise in addressing this problem, but they are limited by a lack of large labeled training datasets. We propose using imperfect but plentiful archived annotations to train CNNs for automated, real-time EEG seizure detection across patients. While these weak annotations indicate possible seizures with precision scores as low as 0.37, they are commonly produced in large volumes within existing clinical workflows by a mixed group of technicians, fellows, students, and board-certified epileptologists. We find that CNNs trained using such weak annotations achieve Area Under the Receiver Operating Characteristic curve (AUROC) values of 0.93 and 0.94 for pediatric and adult seizure onset detection, respectively. Compared to currently deployed clinical software, our model provides a 31% increase (18 points) in F1-score for pediatric patients and a 17% increase (11 points) for adult patients. These results demonstrate that weak annotations, which are sustainably collected via existing clinical workflows, can be leveraged to produce clinically useful seizure detection models.
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spelling pubmed-71708802020-04-29 Weak supervision as an efficient approach for automated seizure detection in electroencephalography Saab, Khaled Dunnmon, Jared Ré, Christopher Rubin, Daniel Lee-Messer, Christopher NPJ Digit Med Article Automated seizure detection from electroencephalography (EEG) would improve the quality of patient care while reducing medical costs, but achieving reliably high performance across patients has proven difficult. Convolutional Neural Networks (CNNs) show promise in addressing this problem, but they are limited by a lack of large labeled training datasets. We propose using imperfect but plentiful archived annotations to train CNNs for automated, real-time EEG seizure detection across patients. While these weak annotations indicate possible seizures with precision scores as low as 0.37, they are commonly produced in large volumes within existing clinical workflows by a mixed group of technicians, fellows, students, and board-certified epileptologists. We find that CNNs trained using such weak annotations achieve Area Under the Receiver Operating Characteristic curve (AUROC) values of 0.93 and 0.94 for pediatric and adult seizure onset detection, respectively. Compared to currently deployed clinical software, our model provides a 31% increase (18 points) in F1-score for pediatric patients and a 17% increase (11 points) for adult patients. These results demonstrate that weak annotations, which are sustainably collected via existing clinical workflows, can be leveraged to produce clinically useful seizure detection models. Nature Publishing Group UK 2020-04-20 /pmc/articles/PMC7170880/ /pubmed/32352037 http://dx.doi.org/10.1038/s41746-020-0264-0 Text en © The Author(s) 2020 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.
spellingShingle Article
Saab, Khaled
Dunnmon, Jared
Ré, Christopher
Rubin, Daniel
Lee-Messer, Christopher
Weak supervision as an efficient approach for automated seizure detection in electroencephalography
title Weak supervision as an efficient approach for automated seizure detection in electroencephalography
title_full Weak supervision as an efficient approach for automated seizure detection in electroencephalography
title_fullStr Weak supervision as an efficient approach for automated seizure detection in electroencephalography
title_full_unstemmed Weak supervision as an efficient approach for automated seizure detection in electroencephalography
title_short Weak supervision as an efficient approach for automated seizure detection in electroencephalography
title_sort weak supervision as an efficient approach for automated seizure detection in electroencephalography
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7170880/
https://www.ncbi.nlm.nih.gov/pubmed/32352037
http://dx.doi.org/10.1038/s41746-020-0264-0
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