Modernizing MDD Diagnosis using Deep Learning from EEG Data

Autores
Lebedinsky, Milena; Leguizamon, Rocío; Pytel, Pablo; Chatterjee, P.; Pollo Cattaneo, María Florencia
Año de publicación
2024
Idioma
inglés
Tipo de recurso
documento de conferencia
Estado
versión publicada
Descripción
Major depressive disorder (MDD) is a widespread illness significantly impacting individuals’ quality of life. Its diagnosis through Electroencephalogram (EEG) has long been studied in mental health research. Recent advancements in deep learning present a promising pathway for enhancing MDD diagnosis through EEGs. This study integrates state-of-the-art deep learning techniques, including ConvNext and Transformers architectures, into MDD prediction models. Results demonstrate ConvNext models’ robustness and efficiency, in terms of precision and specificity, while Transformer models exhibit high recall and sensitivity for diagnosing MDD from incomplete studies.
Facultad de Informática
Materia
Ciencias Informáticas
Major Depression Disorder
Electroencephalogram
Deep Learning
Nivel de accesibilidad
acceso abierto
Condiciones de uso
http://creativecommons.org/licenses/by-nc-sa/4.0/
Repositorio
SEDICI (UNLP)
Institución
Universidad Nacional de La Plata
OAI Identificador
oai:sedici.unlp.edu.ar:10915/171706
Acceder
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network_name_str SEDICI (UNLP)
spelling Modernizing MDD Diagnosis using Deep Learning from EEG DataLebedinsky, MilenaLeguizamon, RocíoPytel, PabloChatterjee, P.Pollo Cattaneo, María FlorenciaCiencias InformáticasMajor Depression DisorderElectroencephalogramDeep LearningMajor depressive disorder (MDD) is a widespread illness significantly impacting individuals’ quality of life. Its diagnosis through Electroencephalogram (EEG) has long been studied in mental health research. Recent advancements in deep learning present a promising pathway for enhancing MDD diagnosis through EEGs. This study integrates state-of-the-art deep learning techniques, including ConvNext and Transformers architectures, into MDD prediction models. Results demonstrate ConvNext models’ robustness and efficiency, in terms of precision and specificity, while Transformer models exhibit high recall and sensitivity for diagnosing MDD from incomplete studies.Facultad de Informática2024-06info:eu-repo/semantics/conferenceObjectinfo:eu-repo/semantics/publishedVersionObjeto de conferenciahttp://purl.org/coar/resource_type/c_5794info:ar-repo/semantics/documentoDeConferenciaapplication/pdf1-5http://sedici.unlp.edu.ar/handle/10915/171706enginfo:eu-repo/semantics/altIdentifier/isbn/978-950-34-2413-1info:eu-repo/semantics/reference/url/https://sedici.unlp.edu.ar/handle/10915/171300info:eu-repo/semantics/openAccesshttp://creativecommons.org/licenses/by-nc-sa/4.0/Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)reponame:SEDICI (UNLP)instname:Universidad Nacional de La Platainstacron:UNLP2026-05-06T12:53:34Zoai:sedici.unlp.edu.ar:10915/171706Institucionalhttp://sedici.unlp.edu.ar/Universidad públicaNo correspondehttp://sedici.unlp.edu.ar/oai/snrdalira@sedici.unlp.edu.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:13292026-05-06 12:53:34.965SEDICI (UNLP) - Universidad Nacional de La Platafalse
dc.title.none.fl_str_mv Modernizing MDD Diagnosis using Deep Learning from EEG Data
title Modernizing MDD Diagnosis using Deep Learning from EEG Data
spellingShingle Modernizing MDD Diagnosis using Deep Learning from EEG Data
Lebedinsky, Milena
Ciencias Informáticas
Major Depression Disorder
Electroencephalogram
Deep Learning
title_short Modernizing MDD Diagnosis using Deep Learning from EEG Data
title_full Modernizing MDD Diagnosis using Deep Learning from EEG Data
title_fullStr Modernizing MDD Diagnosis using Deep Learning from EEG Data
title_full_unstemmed Modernizing MDD Diagnosis using Deep Learning from EEG Data
title_sort Modernizing MDD Diagnosis using Deep Learning from EEG Data
dc.creator.none.fl_str_mv Lebedinsky, Milena
Leguizamon, Rocío
Pytel, Pablo
Chatterjee, P.
Pollo Cattaneo, María Florencia
author Lebedinsky, Milena
author_facet Lebedinsky, Milena
Leguizamon, Rocío
Pytel, Pablo
Chatterjee, P.
Pollo Cattaneo, María Florencia
author_role author
author2 Leguizamon, Rocío
Pytel, Pablo
Chatterjee, P.
Pollo Cattaneo, María Florencia
author2_role author
author
author
author
dc.subject.none.fl_str_mv Ciencias Informáticas
Major Depression Disorder
Electroencephalogram
Deep Learning
topic Ciencias Informáticas
Major Depression Disorder
Electroencephalogram
Deep Learning
dc.description.none.fl_txt_mv Major depressive disorder (MDD) is a widespread illness significantly impacting individuals’ quality of life. Its diagnosis through Electroencephalogram (EEG) has long been studied in mental health research. Recent advancements in deep learning present a promising pathway for enhancing MDD diagnosis through EEGs. This study integrates state-of-the-art deep learning techniques, including ConvNext and Transformers architectures, into MDD prediction models. Results demonstrate ConvNext models’ robustness and efficiency, in terms of precision and specificity, while Transformer models exhibit high recall and sensitivity for diagnosing MDD from incomplete studies.
Facultad de Informática
description Major depressive disorder (MDD) is a widespread illness significantly impacting individuals’ quality of life. Its diagnosis through Electroencephalogram (EEG) has long been studied in mental health research. Recent advancements in deep learning present a promising pathway for enhancing MDD diagnosis through EEGs. This study integrates state-of-the-art deep learning techniques, including ConvNext and Transformers architectures, into MDD prediction models. Results demonstrate ConvNext models’ robustness and efficiency, in terms of precision and specificity, while Transformer models exhibit high recall and sensitivity for diagnosing MDD from incomplete studies.
publishDate 2024
dc.date.none.fl_str_mv 2024-06
dc.type.none.fl_str_mv info:eu-repo/semantics/conferenceObject
info:eu-repo/semantics/publishedVersion
Objeto de conferencia
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dc.identifier.none.fl_str_mv http://sedici.unlp.edu.ar/handle/10915/171706
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dc.language.none.fl_str_mv eng
language eng
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info:eu-repo/semantics/reference/url/https://sedici.unlp.edu.ar/handle/10915/171300
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
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Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)
eu_rights_str_mv openAccess
rights_invalid_str_mv http://creativecommons.org/licenses/by-nc-sa/4.0/
Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)
dc.format.none.fl_str_mv application/pdf
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repository.name.fl_str_mv SEDICI (UNLP) - Universidad Nacional de La Plata
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