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An end-to-end seizure prediction approach using long short-term memory network

There are increasing epilepsy patients suffering from the pain of seizure onsets, and effective prediction of seizures could improve their quality of life. To obtain high sensitivity for epileptic seizure prediction, current studies generally need complex feature extraction operations, which heavily...

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Autores principales: Wu, Xiao, Yang, Zhaohui, Zhang, Tinglin, Zhang, Limei, Qiao, Lishan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10232837/
https://www.ncbi.nlm.nih.gov/pubmed/37275341
http://dx.doi.org/10.3389/fnhum.2023.1187794
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author Wu, Xiao
Yang, Zhaohui
Zhang, Tinglin
Zhang, Limei
Qiao, Lishan
author_facet Wu, Xiao
Yang, Zhaohui
Zhang, Tinglin
Zhang, Limei
Qiao, Lishan
author_sort Wu, Xiao
collection PubMed
description There are increasing epilepsy patients suffering from the pain of seizure onsets, and effective prediction of seizures could improve their quality of life. To obtain high sensitivity for epileptic seizure prediction, current studies generally need complex feature extraction operations, which heavily depends on the artificial experience (or domain knowledge) and is highly subjective. To address these issues, in this paper we propose an end-to-end epileptic seizure prediction approach based on the long short-term memory network (LSTM). In the new method, only the gamma band of raw electroencephalography (EEG) signals is extracted as network input directly for seizure prediction, thus avoiding subjective and expensive feature design process. Despite its simplicity, the proposed method achieves the mean sensitivity of 91.76% and false prediction rate (FPR) of 0.29/h on Children’s Hospital Boston-MIT (CHB-MIT) scalp EEG Database, respectively, when identifying the preictal stage from the EEG signals. Furthermore, different from traditional methods that only consider the classification of preictal and interictal EEG, we introduce the postictal stage as an extra class in the proposed method. As a result, the performance of seizure prediction is further improved, obtaining a higher sensitivity of 92.17% and a low FPR of 0.27/h. The mean warning time is 44.46 min, which suggests that sufficient time is reserved for patients to take intervention measures by this prediction method.
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spelling pubmed-102328372023-06-02 An end-to-end seizure prediction approach using long short-term memory network Wu, Xiao Yang, Zhaohui Zhang, Tinglin Zhang, Limei Qiao, Lishan Front Hum Neurosci Neuroscience There are increasing epilepsy patients suffering from the pain of seizure onsets, and effective prediction of seizures could improve their quality of life. To obtain high sensitivity for epileptic seizure prediction, current studies generally need complex feature extraction operations, which heavily depends on the artificial experience (or domain knowledge) and is highly subjective. To address these issues, in this paper we propose an end-to-end epileptic seizure prediction approach based on the long short-term memory network (LSTM). In the new method, only the gamma band of raw electroencephalography (EEG) signals is extracted as network input directly for seizure prediction, thus avoiding subjective and expensive feature design process. Despite its simplicity, the proposed method achieves the mean sensitivity of 91.76% and false prediction rate (FPR) of 0.29/h on Children’s Hospital Boston-MIT (CHB-MIT) scalp EEG Database, respectively, when identifying the preictal stage from the EEG signals. Furthermore, different from traditional methods that only consider the classification of preictal and interictal EEG, we introduce the postictal stage as an extra class in the proposed method. As a result, the performance of seizure prediction is further improved, obtaining a higher sensitivity of 92.17% and a low FPR of 0.27/h. The mean warning time is 44.46 min, which suggests that sufficient time is reserved for patients to take intervention measures by this prediction method. Frontiers Media S.A. 2023-05-18 /pmc/articles/PMC10232837/ /pubmed/37275341 http://dx.doi.org/10.3389/fnhum.2023.1187794 Text en Copyright © 2023 Wu, Yang, Zhang, Zhang and Qiao. 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
Wu, Xiao
Yang, Zhaohui
Zhang, Tinglin
Zhang, Limei
Qiao, Lishan
An end-to-end seizure prediction approach using long short-term memory network
title An end-to-end seizure prediction approach using long short-term memory network
title_full An end-to-end seizure prediction approach using long short-term memory network
title_fullStr An end-to-end seizure prediction approach using long short-term memory network
title_full_unstemmed An end-to-end seizure prediction approach using long short-term memory network
title_short An end-to-end seizure prediction approach using long short-term memory network
title_sort end-to-end seizure prediction approach using long short-term memory network
topic Neuroscience
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10232837/
https://www.ncbi.nlm.nih.gov/pubmed/37275341
http://dx.doi.org/10.3389/fnhum.2023.1187794
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