The sound of silence: Quantification of typical absence seizures by sonifying EEG signals from a custom‐built wearable device

Detalhes bibliográficos
Autor(a) principal: Borges, Daniel Filipe
Data de Publicação: 2024
Outros Autores: Fernandes, João, Soares, Joana Isabel, Casalta‐Lopes, João, Carvalho, Daniel, Beniczky, Sándor, Leal, Alberto
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: http://hdl.handle.net/10400.22/26247
Resumo: Objective: To develop and validate a method for long- term (24- h) objective quantification of absence seizures in the EEG of patients with childhood absence epilepsy (CAE) in their real home environment using a wearable device (waEEG), comparing automatic detection methods with auditory recognition after seizure sonification. Methods: The waEEG recording was acquired with two scalp electrodes. Automatic analysis was performed using previously validated software (Persyst® 14) and then fully reviewed by an experienced clinical neurophysiologist. The EEG data were converted into an audio file in waveform format with a 60- fold time compression factor. The sonified EEG was listened to by three inexperienced observers and the number of seizures and the processing time required for each data set were recorded blind to other data. Quantification of seizures from the patient diary was also assessed. Results: Eleven waEEG recordings from seven CAE patients with an average age of 8.18 ± 1.60 years were included. No differences in the number of seizures were found between the recordings using automated methods and expert audio assessment, with significant correlations between methods (ρ > .89, p < .001) and between observers (ρ > .96, p < .001). For the entire data set, the audio assessment yielded a sensitivity of .830 and a precision of .841, resulting in an F1 score of .835. Significance: Auditory waEEG seizure detection by lay medical personnel provided similar accuracy to post- processed automatic detection by an experienced clinical neurophysiologist, but in a less time- consuming procedure and without the need for specialized resources. Sonification of long- term EEG recordings in CAE provides a user- friendly and cost- effective clinical workflow for quantifying seizures in clinical practice, minimizing human and technical constraints.
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spelling The sound of silence: Quantification of typical absence seizures by sonifying EEG signals from a custom‐built wearable deviceEEGEpilepsyObjective: To develop and validate a method for long- term (24- h) objective quantification of absence seizures in the EEG of patients with childhood absence epilepsy (CAE) in their real home environment using a wearable device (waEEG), comparing automatic detection methods with auditory recognition after seizure sonification. Methods: The waEEG recording was acquired with two scalp electrodes. Automatic analysis was performed using previously validated software (Persyst® 14) and then fully reviewed by an experienced clinical neurophysiologist. The EEG data were converted into an audio file in waveform format with a 60- fold time compression factor. The sonified EEG was listened to by three inexperienced observers and the number of seizures and the processing time required for each data set were recorded blind to other data. Quantification of seizures from the patient diary was also assessed. Results: Eleven waEEG recordings from seven CAE patients with an average age of 8.18 ± 1.60 years were included. No differences in the number of seizures were found between the recordings using automated methods and expert audio assessment, with significant correlations between methods (ρ > .89, p < .001) and between observers (ρ > .96, p < .001). For the entire data set, the audio assessment yielded a sensitivity of .830 and a precision of .841, resulting in an F1 score of .835. Significance: Auditory waEEG seizure detection by lay medical personnel provided similar accuracy to post- processed automatic detection by an experienced clinical neurophysiologist, but in a less time- consuming procedure and without the need for specialized resources. Sonification of long- term EEG recordings in CAE provides a user- friendly and cost- effective clinical workflow for quantifying seizures in clinical practice, minimizing human and technical constraints.Repositório Científico do Instituto Politécnico do PortoBorges, Daniel FilipeFernandes, JoãoSoares, Joana IsabelCasalta‐Lopes, JoãoCarvalho, DanielBeniczky, SándorLeal, Alberto2024-10-15T14:28:23Z20242024-01-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10400.22/26247engBorges DF, Fernandes J, Soares JI, Casalta- Lopes J, Carvalho D, Beniczky S, et al. The sound of silence: Quantification of typical absence seizures by sonifying EEG signals from a custom- built wearable device. Epileptic Disord. 2024;26:188–198. https://doi.org/10.1002/epd2.2019410.1002/epd2.201941950-6945metadata only accessinfo:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2024-10-23T01:47:06Zoai:recipp.ipp.pt:10400.22/26247Portal AgregadorONGhttps://www.rcaap.pt/oai/openairemluisa.alvim@gmail.comopendoar:71602024-10-23T01:47:06Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse
dc.title.none.fl_str_mv The sound of silence: Quantification of typical absence seizures by sonifying EEG signals from a custom‐built wearable device
title The sound of silence: Quantification of typical absence seizures by sonifying EEG signals from a custom‐built wearable device
spellingShingle The sound of silence: Quantification of typical absence seizures by sonifying EEG signals from a custom‐built wearable device
Borges, Daniel Filipe
EEG
Epilepsy
title_short The sound of silence: Quantification of typical absence seizures by sonifying EEG signals from a custom‐built wearable device
title_full The sound of silence: Quantification of typical absence seizures by sonifying EEG signals from a custom‐built wearable device
title_fullStr The sound of silence: Quantification of typical absence seizures by sonifying EEG signals from a custom‐built wearable device
title_full_unstemmed The sound of silence: Quantification of typical absence seizures by sonifying EEG signals from a custom‐built wearable device
title_sort The sound of silence: Quantification of typical absence seizures by sonifying EEG signals from a custom‐built wearable device
author Borges, Daniel Filipe
author_facet Borges, Daniel Filipe
Fernandes, João
Soares, Joana Isabel
Casalta‐Lopes, João
Carvalho, Daniel
Beniczky, Sándor
Leal, Alberto
author_role author
author2 Fernandes, João
Soares, Joana Isabel
Casalta‐Lopes, João
Carvalho, Daniel
Beniczky, Sándor
Leal, Alberto
author2_role author
author
author
author
author
author
dc.contributor.none.fl_str_mv Repositório Científico do Instituto Politécnico do Porto
dc.contributor.author.fl_str_mv Borges, Daniel Filipe
Fernandes, João
Soares, Joana Isabel
Casalta‐Lopes, João
Carvalho, Daniel
Beniczky, Sándor
Leal, Alberto
dc.subject.por.fl_str_mv EEG
Epilepsy
topic EEG
Epilepsy
description Objective: To develop and validate a method for long- term (24- h) objective quantification of absence seizures in the EEG of patients with childhood absence epilepsy (CAE) in their real home environment using a wearable device (waEEG), comparing automatic detection methods with auditory recognition after seizure sonification. Methods: The waEEG recording was acquired with two scalp electrodes. Automatic analysis was performed using previously validated software (Persyst® 14) and then fully reviewed by an experienced clinical neurophysiologist. The EEG data were converted into an audio file in waveform format with a 60- fold time compression factor. The sonified EEG was listened to by three inexperienced observers and the number of seizures and the processing time required for each data set were recorded blind to other data. Quantification of seizures from the patient diary was also assessed. Results: Eleven waEEG recordings from seven CAE patients with an average age of 8.18 ± 1.60 years were included. No differences in the number of seizures were found between the recordings using automated methods and expert audio assessment, with significant correlations between methods (ρ > .89, p < .001) and between observers (ρ > .96, p < .001). For the entire data set, the audio assessment yielded a sensitivity of .830 and a precision of .841, resulting in an F1 score of .835. Significance: Auditory waEEG seizure detection by lay medical personnel provided similar accuracy to post- processed automatic detection by an experienced clinical neurophysiologist, but in a less time- consuming procedure and without the need for specialized resources. Sonification of long- term EEG recordings in CAE provides a user- friendly and cost- effective clinical workflow for quantifying seizures in clinical practice, minimizing human and technical constraints.
publishDate 2024
dc.date.none.fl_str_mv 2024-10-15T14:28:23Z
2024
2024-01-01T00:00:00Z
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
format article
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://hdl.handle.net/10400.22/26247
url http://hdl.handle.net/10400.22/26247
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv Borges DF, Fernandes J, Soares JI, Casalta- Lopes J, Carvalho D, Beniczky S, et al. The sound of silence: Quantification of typical absence seizures by sonifying EEG signals from a custom- built wearable device. Epileptic Disord. 2024;26:188–198. https://doi.org/10.1002/epd2.20194
10.1002/epd2.20194
1950-6945
dc.rights.driver.fl_str_mv metadata only access
info:eu-repo/semantics/openAccess
rights_invalid_str_mv metadata only access
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.source.none.fl_str_mv reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
instacron:RCAAP
instname_str Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
instacron_str RCAAP
institution RCAAP
reponame_str Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
collection Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
repository.name.fl_str_mv Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
repository.mail.fl_str_mv mluisa.alvim@gmail.com
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