Modernizing MDD Diagnosis using Deep Learning from EEG Data

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...

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Autores principales: Lebedinsky, Milena, Leguizamon, Rocío, Pytel, Pablo, Chatterjee, P., Pollo Cattaneo, María Florencia
Formato: Objeto de conferencia
Lenguaje:Inglés
Publicado: 2024
Materias:
Ciencias Informáticas
Major Depression Disorder
Electroencephalogram
Deep Learning
Acceso en línea:http://sedici.unlp.edu.ar/handle/10915/171706
Aporte de:
SEDICI (UNLP) de Universidad Nacional de La Plata
id I19-R120-10915-171706
record_format dspace
spelling I19-R120-10915-1717062024-10-21T20:03:02Z http://sedici.unlp.edu.ar/handle/10915/171706 Modernizing MDD Diagnosis using Deep Learning from EEG Data Lebedinsky, Milena Leguizamon, Rocío Pytel, Pablo Chatterjee, P. Pollo Cattaneo, María Florencia 2024-06 2024 2024-10-21T12:41:34Z en Ciencias Informáticas Major Depression Disorder Electroencephalogram Deep Learning 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 Objeto de conferencia Objeto de conferencia http://creativecommons.org/licenses/by-nc-sa/4.0/ Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0) application/pdf 1-5
institution Universidad Nacional de La Plata
institution_str I-19
repository_str R-120
collection SEDICI (UNLP)
language Inglés
topic Ciencias Informáticas
Major Depression Disorder
Electroencephalogram
Deep Learning
spellingShingle Ciencias Informáticas
Major Depression Disorder
Electroencephalogram
Deep Learning
Lebedinsky, Milena
Leguizamon, Rocío
Pytel, Pablo
Chatterjee, P.
Pollo Cattaneo, María Florencia
Modernizing MDD Diagnosis using Deep Learning from EEG Data
topic_facet Ciencias Informáticas
Major Depression Disorder
Electroencephalogram
Deep Learning
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.
format Objeto de conferencia
Objeto de conferencia
author Lebedinsky, Milena
Leguizamon, Rocío
Pytel, Pablo
Chatterjee, P.
Pollo Cattaneo, María Florencia
author_facet Lebedinsky, Milena
Leguizamon, Rocío
Pytel, Pablo
Chatterjee, P.
Pollo Cattaneo, María Florencia
author_sort Lebedinsky, Milena
title Modernizing MDD Diagnosis using Deep Learning from EEG Data
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
publishDate 2024
url http://sedici.unlp.edu.ar/handle/10915/171706
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AT leguizamonrocio modernizingmdddiagnosisusingdeeplearningfromeegdata
AT pytelpablo modernizingmdddiagnosisusingdeeplearningfromeegdata
AT chatterjeep modernizingmdddiagnosisusingdeeplearningfromeegdata
AT pollocattaneomariaflorencia modernizingmdddiagnosisusingdeeplearningfromeegdata
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