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A Two-Stage Automatic System for Detection of Interictal Epileptiform Discharges from Scalp Electroencephalograms
The objective of this work was to develop a deep learning-based automatic system with reliable performance in detecting interictal epileptiform discharges (IEDs) from scalp electroencephalograms (EEGs). For the present study, 484 raw scalp EEG recordings were included, standardized, and split into 4...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Society for Neuroscience
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10668214/ https://www.ncbi.nlm.nih.gov/pubmed/37914407 http://dx.doi.org/10.1523/ENEURO.0111-23.2023 |
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author | Wang, Xiaoyun Wang, Xing Wang, Chong Wang, Zhongyuan Liu, Xiangyu Lv, Xiaoling Tang, Ying |
author_facet | Wang, Xiaoyun Wang, Xing Wang, Chong Wang, Zhongyuan Liu, Xiangyu Lv, Xiaoling Tang, Ying |
author_sort | Wang, Xiaoyun |
collection | PubMed |
description | The objective of this work was to develop a deep learning-based automatic system with reliable performance in detecting interictal epileptiform discharges (IEDs) from scalp electroencephalograms (EEGs). For the present study, 484 raw scalp EEG recordings were included, standardized, and split into 406 for training and 78 for testing. Two neurophysiologists individually annotated the recordings for training in channel-wise manner. Annotations were divided into segments, on which nine deep neural networks (DNNs) were trained for the multiclassification of IED, artifact, and background. The fitted IED detectors were then evaluated on 78 EEG recordings with IED events fully annotated by three experts independently (majority agreement). A two montage-based decision mechanism (TMDM) was designed to determine whether an IED event occurred at a single time instant. Area under the precision–recall curve (AUPRC), as well as false-positive rates, F1 scores, and kappa agreement scores for sensitivity = 0.8 were estimated. In multitype classification, five DNNs provided one-versus-rest AUPRC mean value >0.993 using fivefold cross-validation. In IED detection, the system that had integrated the temporal convolutional network (TCN)-based IED detector and the TMDM rule achieved an AUPRC of 0.811. The false positive was 0.194/min (11.64/h), and the F1 score was 0.745. The agreement score between the system and the experts was 0.905. The proposed framework provides a TCN-based IED detector and a novel two montage-based determining mechanism that combined to make an automatic IED detection system. The system would be useful in aiding clinic EEG interpretation. |
format | Online Article Text |
id | pubmed-10668214 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Society for Neuroscience |
record_format | MEDLINE/PubMed |
spelling | pubmed-106682142023-11-16 A Two-Stage Automatic System for Detection of Interictal Epileptiform Discharges from Scalp Electroencephalograms Wang, Xiaoyun Wang, Xing Wang, Chong Wang, Zhongyuan Liu, Xiangyu Lv, Xiaoling Tang, Ying eNeuro Research Article: Methods/New Tools The objective of this work was to develop a deep learning-based automatic system with reliable performance in detecting interictal epileptiform discharges (IEDs) from scalp electroencephalograms (EEGs). For the present study, 484 raw scalp EEG recordings were included, standardized, and split into 406 for training and 78 for testing. Two neurophysiologists individually annotated the recordings for training in channel-wise manner. Annotations were divided into segments, on which nine deep neural networks (DNNs) were trained for the multiclassification of IED, artifact, and background. The fitted IED detectors were then evaluated on 78 EEG recordings with IED events fully annotated by three experts independently (majority agreement). A two montage-based decision mechanism (TMDM) was designed to determine whether an IED event occurred at a single time instant. Area under the precision–recall curve (AUPRC), as well as false-positive rates, F1 scores, and kappa agreement scores for sensitivity = 0.8 were estimated. In multitype classification, five DNNs provided one-versus-rest AUPRC mean value >0.993 using fivefold cross-validation. In IED detection, the system that had integrated the temporal convolutional network (TCN)-based IED detector and the TMDM rule achieved an AUPRC of 0.811. The false positive was 0.194/min (11.64/h), and the F1 score was 0.745. The agreement score between the system and the experts was 0.905. The proposed framework provides a TCN-based IED detector and a novel two montage-based determining mechanism that combined to make an automatic IED detection system. The system would be useful in aiding clinic EEG interpretation. Society for Neuroscience 2023-11-16 /pmc/articles/PMC10668214/ /pubmed/37914407 http://dx.doi.org/10.1523/ENEURO.0111-23.2023 Text en Copyright © 2023 Wang et al. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution 4.0 International license (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution and reproduction in any medium provided that the original work is properly attributed. |
spellingShingle | Research Article: Methods/New Tools Wang, Xiaoyun Wang, Xing Wang, Chong Wang, Zhongyuan Liu, Xiangyu Lv, Xiaoling Tang, Ying A Two-Stage Automatic System for Detection of Interictal Epileptiform Discharges from Scalp Electroencephalograms |
title | A Two-Stage Automatic System for Detection of Interictal Epileptiform Discharges from Scalp Electroencephalograms |
title_full | A Two-Stage Automatic System for Detection of Interictal Epileptiform Discharges from Scalp Electroencephalograms |
title_fullStr | A Two-Stage Automatic System for Detection of Interictal Epileptiform Discharges from Scalp Electroencephalograms |
title_full_unstemmed | A Two-Stage Automatic System for Detection of Interictal Epileptiform Discharges from Scalp Electroencephalograms |
title_short | A Two-Stage Automatic System for Detection of Interictal Epileptiform Discharges from Scalp Electroencephalograms |
title_sort | two-stage automatic system for detection of interictal epileptiform discharges from scalp electroencephalograms |
topic | Research Article: Methods/New Tools |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10668214/ https://www.ncbi.nlm.nih.gov/pubmed/37914407 http://dx.doi.org/10.1523/ENEURO.0111-23.2023 |
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