Epileptic Seizure Detection Using Deep Learning Based Long Short-Term Memory Networks and Time-Frequency Analysis: a Comparative Investigation in Machine Learning Paradigm
Autor(a) principal: | |
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Data de Publicação: | 2022 |
Outros Autores: | , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Brazilian Archives of Biology and Technology |
Texto Completo: | http://old.scielo.br/scielo.php?script=sci_arttext&pid=S1516-89132022000100623 |
Resumo: | Abstract Epilepsy is a noncontagious brain abnormality, which causes electrical distraction and strains the neural system. Generally, epilepsy is treated and diagnosed through continuous examination and interpretation of the electroencephalography (EEG) signals. This is a very time-consuming and tedious job. Further, it is subjected to observational errors and observer variability. Hence, the development of an efficient automatic alarm system to recognize epileptic seizure signals is of important concern. The objectives of the present study are to investigate deep learning based long short term memory (LSTM) networks for the classification of epileptic EEG signals using time-frequency analysis. Additionally, a comparative investigation is carried out to evaluate the various state-of-the-art feature selection and classification models for automatic classification of EEG signals for Epilepsy detection. Features based on statistics, entropy, and fractal were extracted from both the time domain and frequency domain. The extracted features were supplied to LSTM networks and traditional machine learning models for epileptic EEG classification. High classification accuracy of 100% (under hold out and 10-fold protocol) and 99.80% (under 10-fold protocol) is achieved by the proposed LSTM strategy followed by the Back Propagation Artificial Neural network (BPANN) which achieves 99.6% classification accuracy when all the 150 EEG biomarkers were used as input to the classifier under 10-fold cross-validation technique. Further, when the top 30 most relevant features selected by different feature selection techniques are used for classification, the proposed approach achieves similar performance followed by BPANN which reports 99.4% classification accuracy when combined with the Relief F feature selection technique. |
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Brazilian Archives of Biology and Technology |
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Epileptic Seizure Detection Using Deep Learning Based Long Short-Term Memory Networks and Time-Frequency Analysis: a Comparative Investigation in Machine Learning ParadigmEEGepileptic seizurerisk stratificationdeep learninglong term short memory networksmachine learning.Abstract Epilepsy is a noncontagious brain abnormality, which causes electrical distraction and strains the neural system. Generally, epilepsy is treated and diagnosed through continuous examination and interpretation of the electroencephalography (EEG) signals. This is a very time-consuming and tedious job. Further, it is subjected to observational errors and observer variability. Hence, the development of an efficient automatic alarm system to recognize epileptic seizure signals is of important concern. The objectives of the present study are to investigate deep learning based long short term memory (LSTM) networks for the classification of epileptic EEG signals using time-frequency analysis. Additionally, a comparative investigation is carried out to evaluate the various state-of-the-art feature selection and classification models for automatic classification of EEG signals for Epilepsy detection. Features based on statistics, entropy, and fractal were extracted from both the time domain and frequency domain. The extracted features were supplied to LSTM networks and traditional machine learning models for epileptic EEG classification. High classification accuracy of 100% (under hold out and 10-fold protocol) and 99.80% (under 10-fold protocol) is achieved by the proposed LSTM strategy followed by the Back Propagation Artificial Neural network (BPANN) which achieves 99.6% classification accuracy when all the 150 EEG biomarkers were used as input to the classifier under 10-fold cross-validation technique. Further, when the top 30 most relevant features selected by different feature selection techniques are used for classification, the proposed approach achieves similar performance followed by BPANN which reports 99.4% classification accuracy when combined with the Relief F feature selection technique.Instituto de Tecnologia do Paraná - Tecpar2022-01-01info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersiontext/htmlhttp://old.scielo.br/scielo.php?script=sci_arttext&pid=S1516-89132022000100623Brazilian Archives of Biology and Technology v.65 2022reponame:Brazilian Archives of Biology and Technologyinstname:Instituto de Tecnologia do Paraná (Tecpar)instacron:TECPAR10.1590/1678-4324-2022210559info:eu-repo/semantics/openAccessMandal,SunandanSingh,Bikesh KumarThakur,Kavitaeng2022-09-06T00:00:00Zoai:scielo:S1516-89132022000100623Revistahttps://www.scielo.br/j/babt/https://old.scielo.br/oai/scielo-oai.phpbabt@tecpar.br||babt@tecpar.br1678-43241516-8913opendoar:2022-09-06T00:00Brazilian Archives of Biology and Technology - Instituto de Tecnologia do Paraná (Tecpar)false |
dc.title.none.fl_str_mv |
Epileptic Seizure Detection Using Deep Learning Based Long Short-Term Memory Networks and Time-Frequency Analysis: a Comparative Investigation in Machine Learning Paradigm |
title |
Epileptic Seizure Detection Using Deep Learning Based Long Short-Term Memory Networks and Time-Frequency Analysis: a Comparative Investigation in Machine Learning Paradigm |
spellingShingle |
Epileptic Seizure Detection Using Deep Learning Based Long Short-Term Memory Networks and Time-Frequency Analysis: a Comparative Investigation in Machine Learning Paradigm Mandal,Sunandan EEG epileptic seizure risk stratification deep learning long term short memory networks machine learning. |
title_short |
Epileptic Seizure Detection Using Deep Learning Based Long Short-Term Memory Networks and Time-Frequency Analysis: a Comparative Investigation in Machine Learning Paradigm |
title_full |
Epileptic Seizure Detection Using Deep Learning Based Long Short-Term Memory Networks and Time-Frequency Analysis: a Comparative Investigation in Machine Learning Paradigm |
title_fullStr |
Epileptic Seizure Detection Using Deep Learning Based Long Short-Term Memory Networks and Time-Frequency Analysis: a Comparative Investigation in Machine Learning Paradigm |
title_full_unstemmed |
Epileptic Seizure Detection Using Deep Learning Based Long Short-Term Memory Networks and Time-Frequency Analysis: a Comparative Investigation in Machine Learning Paradigm |
title_sort |
Epileptic Seizure Detection Using Deep Learning Based Long Short-Term Memory Networks and Time-Frequency Analysis: a Comparative Investigation in Machine Learning Paradigm |
author |
Mandal,Sunandan |
author_facet |
Mandal,Sunandan Singh,Bikesh Kumar Thakur,Kavita |
author_role |
author |
author2 |
Singh,Bikesh Kumar Thakur,Kavita |
author2_role |
author author |
dc.contributor.author.fl_str_mv |
Mandal,Sunandan Singh,Bikesh Kumar Thakur,Kavita |
dc.subject.por.fl_str_mv |
EEG epileptic seizure risk stratification deep learning long term short memory networks machine learning. |
topic |
EEG epileptic seizure risk stratification deep learning long term short memory networks machine learning. |
description |
Abstract Epilepsy is a noncontagious brain abnormality, which causes electrical distraction and strains the neural system. Generally, epilepsy is treated and diagnosed through continuous examination and interpretation of the electroencephalography (EEG) signals. This is a very time-consuming and tedious job. Further, it is subjected to observational errors and observer variability. Hence, the development of an efficient automatic alarm system to recognize epileptic seizure signals is of important concern. The objectives of the present study are to investigate deep learning based long short term memory (LSTM) networks for the classification of epileptic EEG signals using time-frequency analysis. Additionally, a comparative investigation is carried out to evaluate the various state-of-the-art feature selection and classification models for automatic classification of EEG signals for Epilepsy detection. Features based on statistics, entropy, and fractal were extracted from both the time domain and frequency domain. The extracted features were supplied to LSTM networks and traditional machine learning models for epileptic EEG classification. High classification accuracy of 100% (under hold out and 10-fold protocol) and 99.80% (under 10-fold protocol) is achieved by the proposed LSTM strategy followed by the Back Propagation Artificial Neural network (BPANN) which achieves 99.6% classification accuracy when all the 150 EEG biomarkers were used as input to the classifier under 10-fold cross-validation technique. Further, when the top 30 most relevant features selected by different feature selection techniques are used for classification, the proposed approach achieves similar performance followed by BPANN which reports 99.4% classification accuracy when combined with the Relief F feature selection technique. |
publishDate |
2022 |
dc.date.none.fl_str_mv |
2022-01-01 |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://old.scielo.br/scielo.php?script=sci_arttext&pid=S1516-89132022000100623 |
url |
http://old.scielo.br/scielo.php?script=sci_arttext&pid=S1516-89132022000100623 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
10.1590/1678-4324-2022210559 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
text/html |
dc.publisher.none.fl_str_mv |
Instituto de Tecnologia do Paraná - Tecpar |
publisher.none.fl_str_mv |
Instituto de Tecnologia do Paraná - Tecpar |
dc.source.none.fl_str_mv |
Brazilian Archives of Biology and Technology v.65 2022 reponame:Brazilian Archives of Biology and Technology instname:Instituto de Tecnologia do Paraná (Tecpar) instacron:TECPAR |
instname_str |
Instituto de Tecnologia do Paraná (Tecpar) |
instacron_str |
TECPAR |
institution |
TECPAR |
reponame_str |
Brazilian Archives of Biology and Technology |
collection |
Brazilian Archives of Biology and Technology |
repository.name.fl_str_mv |
Brazilian Archives of Biology and Technology - Instituto de Tecnologia do Paraná (Tecpar) |
repository.mail.fl_str_mv |
babt@tecpar.br||babt@tecpar.br |
_version_ |
1750318281689923584 |