Application of signal classifiers in auditory evoked potentials for the detection of pathological patients

Autores
Baldiviezo, M. G.; Barbería, J.L.; Bontempo, C. B.; Corsaro, Y.; Fernández Biancardi, F.; Hernando, M. R.; Licata Caruso, L.; Paglia, A.; Rodríguez, M. R.; Legnani, W. E.
Año de publicación
2023
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
The auditory brainstem response (ABR) by evoked potentials is a widespread auditory pathway assessment technique. This is largely applied due to its cost-effectiveness, practicality and ease of use. In contrast, it requires a trained professional to carry out the analysis of the results. This motivates several research efforts to increase the independence of the diagnostician. To this end, the present work shows the ability of three signal classification tools to differentiate ABR studies of normal hearing subjects from those who may have some pathology. As a starting point, the PhysioNet short term auditory evoked potentials databases are used to calculate the features later applied to construct the dataset. The features used are diverse classes of permutation entropy, fractal dimension, the Lyapunov exponent and the zero crossing rate. To ensure more accurate results, a Montecarlo simulation of one thousand trials is employed to train the
UTN FRBA
Fil: Baldiviezo, M. G. Facultad Regional Buenos Aires. Signal and Image Processing Center; Argentina.
Fil: Barbería, J.L. Facultad Regional Buenos Aires. Signal and Image Processing Center; Argentina.
Fil: Bontempo, C. B. Facultad Regional Buenos Aires. Signal and Image Processing Center; Argentina.
Fil: Corsaro, Y. Facultad Regional Buenos Aires. Signal and Image Processing Center; Argentina.
Fil: Fernández Biancardi, F. Facultad Regional Buenos Aires. Signal and Image Processing Center; Argentina.
Fil: Hernando, M. R. Facultad Regional Buenos Aires. Signal and Image Processing Center; Argentina.
Fil: Licata Caruso, L. Facultad Regional Buenos Aires. Signal and Image Processing Center; Argentina.
Fil: Paglia, A. Facultad Regional Buenos Aires. Signal and Image Processing Center; Argentina.
Fil: Rodríguez, M. R. Facultad Regional Buenos Aires. Signal and Image Processing Center; Argentina
Fil: Legnani, W. E. Facultad Regional Buenos Aires. Signal and Image Processing Center; Argentina.
Peer Reviewed
Fuente
Trends in Computational and Applied Mathematics, 24 (1), 63-81. (2023)
Materia
auditory evoked potentials
permutation entropy
signal classification
fractal dimension
Lyapunov exponent
zero crossing rate
support vector machines
random forest
K-nearest neighbours
Nivel de accesibilidad
acceso abierto
Condiciones de uso
2024-03-19T20:26:38Z
Repositorio
Repositorio Institucional Abierto (UTN)
Institución
Universidad Tecnológica Nacional
OAI Identificador
oai:ria.utn.edu.ar:20.500.12272/9922
Acceder
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network_acronym_str RIAUTN
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network_name_str Repositorio Institucional Abierto (UTN)
spelling Application of signal classifiers in auditory evoked potentials for the detection of pathological patientsBaldiviezo, M. G.Barbería, J.L.Bontempo, C. B.Corsaro, Y.Fernández Biancardi, F.Hernando, M. R.Licata Caruso, L.Paglia, A.Rodríguez, M. R.Legnani, W. E.auditory evoked potentialspermutation entropysignal classificationfractal dimensionLyapunov exponentzero crossing ratesupport vector machinesrandom forestK-nearest neighboursThe auditory brainstem response (ABR) by evoked potentials is a widespread auditory pathway assessment technique. This is largely applied due to its cost-effectiveness, practicality and ease of use. In contrast, it requires a trained professional to carry out the analysis of the results. This motivates several research efforts to increase the independence of the diagnostician. To this end, the present work shows the ability of three signal classification tools to differentiate ABR studies of normal hearing subjects from those who may have some pathology. As a starting point, the PhysioNet short term auditory evoked potentials databases are used to calculate the features later applied to construct the dataset. The features used are diverse classes of permutation entropy, fractal dimension, the Lyapunov exponent and the zero crossing rate. To ensure more accurate results, a Montecarlo simulation of one thousand trials is employed to train theUTN FRBAFil: Baldiviezo, M. G. Facultad Regional Buenos Aires. Signal and Image Processing Center; Argentina.Fil: Barbería, J.L. Facultad Regional Buenos Aires. Signal and Image Processing Center; Argentina.Fil: Bontempo, C. B. Facultad Regional Buenos Aires. Signal and Image Processing Center; Argentina.Fil: Corsaro, Y. Facultad Regional Buenos Aires. Signal and Image Processing Center; Argentina.Fil: Fernández Biancardi, F. Facultad Regional Buenos Aires. Signal and Image Processing Center; Argentina.Fil: Hernando, M. R. Facultad Regional Buenos Aires. Signal and Image Processing Center; Argentina.Fil: Licata Caruso, L. Facultad Regional Buenos Aires. Signal and Image Processing Center; Argentina.Fil: Paglia, A. Facultad Regional Buenos Aires. Signal and Image Processing Center; Argentina.Fil: Rodríguez, M. R. Facultad Regional Buenos Aires. Signal and Image Processing Center; ArgentinaFil: Legnani, W. E. Facultad Regional Buenos Aires. Signal and Image Processing Center; Argentina.Peer Reviewed2024-03-19T20:26:38Z2024-03-19T20:26:38Z2023-01-01info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articulopdfapplication/pdfTrends in Computational and Applied Mathematics, 24http://hdl.handle.net/20.500.12272/992210.5540/tcam.2022.024.01.00063Trends in Computational and Applied Mathematics, 24 (1), 63-81. (2023)reponame:Repositorio Institucional Abierto (UTN)instname:Universidad Tecnológica NacionalengASTCABA0008120info:eu-repo/semantics/openAccess2024-03-19T20:26:38Zhttp://creativecommons.org/licenses/by-nc-sa/4.0/Atribución-NoComercial-CompartirIgual 4.0 InternacionalM . G. BALDIVIEZO, J. L. BARBERIA, C. B. BONTEMPO, Y. CORSARO, F. FERNANDEZ BIANCARDI, M. R. HERNANDO, L. LICAT A CARUSO, A. PAGLIA, M. R. RODRIGUEZ, W. E. LEGNANILicencia Creative Commons Atribución- No Comercial2026-09-24T12:45:24Zoai:ria.utn.edu.ar:20.500.12272/9922instacron:UTNInstitucionalhttp://ria.utn.edu.ar/Universidad públicaNo correspondehttp://ria.utn.edu.ar/oaigestionria@rec.utn.edu.ar; fsuarez@rec.utn.edu.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:a2026-09-24 12:45:26.014Repositorio Institucional Abierto (UTN) - Universidad Tecnológica Nacionalfalse
dc.title.none.fl_str_mv Application of signal classifiers in auditory evoked potentials for the detection of pathological patients
title Application of signal classifiers in auditory evoked potentials for the detection of pathological patients
spellingShingle Application of signal classifiers in auditory evoked potentials for the detection of pathological patients
Baldiviezo, M. G.
auditory evoked potentials
permutation entropy
signal classification
fractal dimension
Lyapunov exponent
zero crossing rate
support vector machines
random forest
K-nearest neighbours
title_short Application of signal classifiers in auditory evoked potentials for the detection of pathological patients
title_full Application of signal classifiers in auditory evoked potentials for the detection of pathological patients
title_fullStr Application of signal classifiers in auditory evoked potentials for the detection of pathological patients
title_full_unstemmed Application of signal classifiers in auditory evoked potentials for the detection of pathological patients
title_sort Application of signal classifiers in auditory evoked potentials for the detection of pathological patients
dc.creator.none.fl_str_mv Baldiviezo, M. G.
Barbería, J.L.
Bontempo, C. B.
Corsaro, Y.
Fernández Biancardi, F.
Hernando, M. R.
Licata Caruso, L.
Paglia, A.
Rodríguez, M. R.
Legnani, W. E.
author Baldiviezo, M. G.
author_facet Baldiviezo, M. G.
Barbería, J.L.
Bontempo, C. B.
Corsaro, Y.
Fernández Biancardi, F.
Hernando, M. R.
Licata Caruso, L.
Paglia, A.
Rodríguez, M. R.
Legnani, W. E.
author_role author
author2 Barbería, J.L.
Bontempo, C. B.
Corsaro, Y.
Fernández Biancardi, F.
Hernando, M. R.
Licata Caruso, L.
Paglia, A.
Rodríguez, M. R.
Legnani, W. E.
author2_role author
author
author
author
author
author
author
author
author
dc.subject.none.fl_str_mv auditory evoked potentials
permutation entropy
signal classification
fractal dimension
Lyapunov exponent
zero crossing rate
support vector machines
random forest
K-nearest neighbours
topic auditory evoked potentials
permutation entropy
signal classification
fractal dimension
Lyapunov exponent
zero crossing rate
support vector machines
random forest
K-nearest neighbours
dc.description.none.fl_txt_mv The auditory brainstem response (ABR) by evoked potentials is a widespread auditory pathway assessment technique. This is largely applied due to its cost-effectiveness, practicality and ease of use. In contrast, it requires a trained professional to carry out the analysis of the results. This motivates several research efforts to increase the independence of the diagnostician. To this end, the present work shows the ability of three signal classification tools to differentiate ABR studies of normal hearing subjects from those who may have some pathology. As a starting point, the PhysioNet short term auditory evoked potentials databases are used to calculate the features later applied to construct the dataset. The features used are diverse classes of permutation entropy, fractal dimension, the Lyapunov exponent and the zero crossing rate. To ensure more accurate results, a Montecarlo simulation of one thousand trials is employed to train the
UTN FRBA
Fil: Baldiviezo, M. G. Facultad Regional Buenos Aires. Signal and Image Processing Center; Argentina.
Fil: Barbería, J.L. Facultad Regional Buenos Aires. Signal and Image Processing Center; Argentina.
Fil: Bontempo, C. B. Facultad Regional Buenos Aires. Signal and Image Processing Center; Argentina.
Fil: Corsaro, Y. Facultad Regional Buenos Aires. Signal and Image Processing Center; Argentina.
Fil: Fernández Biancardi, F. Facultad Regional Buenos Aires. Signal and Image Processing Center; Argentina.
Fil: Hernando, M. R. Facultad Regional Buenos Aires. Signal and Image Processing Center; Argentina.
Fil: Licata Caruso, L. Facultad Regional Buenos Aires. Signal and Image Processing Center; Argentina.
Fil: Paglia, A. Facultad Regional Buenos Aires. Signal and Image Processing Center; Argentina.
Fil: Rodríguez, M. R. Facultad Regional Buenos Aires. Signal and Image Processing Center; Argentina
Fil: Legnani, W. E. Facultad Regional Buenos Aires. Signal and Image Processing Center; Argentina.
Peer Reviewed
description The auditory brainstem response (ABR) by evoked potentials is a widespread auditory pathway assessment technique. This is largely applied due to its cost-effectiveness, practicality and ease of use. In contrast, it requires a trained professional to carry out the analysis of the results. This motivates several research efforts to increase the independence of the diagnostician. To this end, the present work shows the ability of three signal classification tools to differentiate ABR studies of normal hearing subjects from those who may have some pathology. As a starting point, the PhysioNet short term auditory evoked potentials databases are used to calculate the features later applied to construct the dataset. The features used are diverse classes of permutation entropy, fractal dimension, the Lyapunov exponent and the zero crossing rate. To ensure more accurate results, a Montecarlo simulation of one thousand trials is employed to train the
publishDate 2023
dc.date.none.fl_str_mv 2023-01-01
2024-03-19T20:26:38Z
2024-03-19T20:26:38Z
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
http://purl.org/coar/resource_type/c_6501
info:ar-repo/semantics/articulo
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv Trends in Computational and Applied Mathematics, 24
http://hdl.handle.net/20.500.12272/9922
10.5540/tcam.2022.024.01.00063
identifier_str_mv Trends in Computational and Applied Mathematics, 24
10.5540/tcam.2022.024.01.00063
url http://hdl.handle.net/20.500.12272/9922
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv ASTCABA0008120
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
2024-03-19T20:26:38Z
http://creativecommons.org/licenses/by-nc-sa/4.0/
Atribución-NoComercial-CompartirIgual 4.0 Internacional
M . G. BALDIVIEZO, J. L. BARBERIA, C. B. BONTEMPO, Y. CORSARO, F. FERNANDEZ BIANCARDI, M. R. HERNANDO, L. LICAT A CARUSO, A. PAGLIA, M. R. RODRIGUEZ, W. E. LEGNANI
Licencia Creative Commons Atribución- No Comercial
eu_rights_str_mv openAccess
rights_invalid_str_mv 2024-03-19T20:26:38Z
http://creativecommons.org/licenses/by-nc-sa/4.0/
Atribución-NoComercial-CompartirIgual 4.0 Internacional
M . G. BALDIVIEZO, J. L. BARBERIA, C. B. BONTEMPO, Y. CORSARO, F. FERNANDEZ BIANCARDI, M. R. HERNANDO, L. LICAT A CARUSO, A. PAGLIA, M. R. RODRIGUEZ, W. E. LEGNANI
Licencia Creative Commons Atribución- No Comercial
dc.format.none.fl_str_mv pdf
application/pdf
dc.source.none.fl_str_mv Trends in Computational and Applied Mathematics, 24 (1), 63-81. (2023)
reponame:Repositorio Institucional Abierto (UTN)
instname:Universidad Tecnológica Nacional
reponame_str Repositorio Institucional Abierto (UTN)
collection Repositorio Institucional Abierto (UTN)
instname_str Universidad Tecnológica Nacional
repository.name.fl_str_mv Repositorio Institucional Abierto (UTN) - Universidad Tecnológica Nacional
repository.mail.fl_str_mv gestionria@rec.utn.edu.ar; fsuarez@rec.utn.edu.ar
_version_ 1877230898200444928
score 13.265058

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