Quaternion Neural Network with Temporal Feedback Calculation: Application to Cardiac Vector Velocity during Myocardial Infarction

Autores
Cruces, Pablo Daniel; Correa Prado, Raul Oscar; Laciar Leber, Eric; Arini, Pedro David
Año de publicación
2017
Idioma
inglés
Tipo de recurso
documento de conferencia
Estado
versión publicada
Descripción
Quaternion neural networks have been shownto be useful in image and signal processing applications.Herein, we propose a novel architecture of a neural unit model characterized by its ability of encoding 3-dimensional past information and that facilitates the learning of velocity patterns. We evaluate the implementation of the networkin a study of the cardiac vector velocity and its usefulness in early detection of patients with anterior myocardial infarction. Experimental results show an improvement of the performance in terms of convergence speed and precision when comparing with traditional methods. Furthermore, the network shows successful results in measuring the velocity reduction that is usually observed in vectorcardiogram signals in the presence of myocardial damage. Through a linear discriminant analysis, a pair of 100% / 98% of sensitivity/specificity is met with only two velocity parameters. We conclude that this method is a very promising developmentfor future computational tools devoted to early diagnosis ofheart diseases.
Fil: Cruces, Pablo Daniel. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Saavedra 15. Instituto Argentino de Matemática Alberto Calderón; Argentina
Fil: Correa Prado, Raul Oscar. Universidad Nacional de San Juan. Facultad de Ingeniería. Departamento de Electrónica y Automática. Gabinete de Tecnología Médica; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Laciar Leber, Eric. Universidad Nacional de San Juan. Facultad de Ingeniería. Departamento de Electrónica y Automática. Gabinete de Tecnología Médica; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Arini, Pedro David. Universidad de Buenos Aires. Facultad de Ingeniería. Instituto de Ingeniería Biomédica; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Saavedra 15. Instituto Argentino de Matemática Alberto Calderón; Argentina
XXI Congreso Argentino de Bioingeniería
Córdoba
Argentina
Sociedad Argentina de Bioingeniería
Universidad Nacional de Córdoba
Materia
Myocardial infarction
Quaternion Algebra
Neural Networks
Cardiac vector velocity
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/174568
Acceder
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network_name_str CONICET Digital (CONICET)
spelling Quaternion Neural Network with Temporal Feedback Calculation: Application to Cardiac Vector Velocity during Myocardial InfarctionCruces, Pablo DanielCorrea Prado, Raul OscarLaciar Leber, EricArini, Pedro DavidMyocardial infarctionQuaternion AlgebraNeural NetworksCardiac vector velocityhttps://purl.org/becyt/ford/2.2https://purl.org/becyt/ford/2Quaternion neural networks have been shownto be useful in image and signal processing applications.Herein, we propose a novel architecture of a neural unit model characterized by its ability of encoding 3-dimensional past information and that facilitates the learning of velocity patterns. We evaluate the implementation of the networkin a study of the cardiac vector velocity and its usefulness in early detection of patients with anterior myocardial infarction. Experimental results show an improvement of the performance in terms of convergence speed and precision when comparing with traditional methods. Furthermore, the network shows successful results in measuring the velocity reduction that is usually observed in vectorcardiogram signals in the presence of myocardial damage. Through a linear discriminant analysis, a pair of 100% / 98% of sensitivity/specificity is met with only two velocity parameters. We conclude that this method is a very promising developmentfor future computational tools devoted to early diagnosis ofheart diseases.Fil: Cruces, Pablo Daniel. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Saavedra 15. Instituto Argentino de Matemática Alberto Calderón; ArgentinaFil: Correa Prado, Raul Oscar. Universidad Nacional de San Juan. Facultad de Ingeniería. Departamento de Electrónica y Automática. Gabinete de Tecnología Médica; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Laciar Leber, Eric. Universidad Nacional de San Juan. Facultad de Ingeniería. Departamento de Electrónica y Automática. Gabinete de Tecnología Médica; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Arini, Pedro David. Universidad de Buenos Aires. Facultad de Ingeniería. Instituto de Ingeniería Biomédica; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Saavedra 15. Instituto Argentino de Matemática Alberto Calderón; ArgentinaXXI Congreso Argentino de BioingenieríaCórdobaArgentinaSociedad Argentina de BioingenieríaUniversidad Nacional de CórdobaSociedad Argentina de Bioingeniería2017info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/conferenceObjectCongresoJournalhttp://purl.org/coar/resource_type/c_5794info:ar-repo/semantics/documentoDeConferenciaapplication/pdfapplication/pdfapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/174568Quaternion Neural Network with Temporal Feedback Calculation: Application to Cardiac Vector Velocity during Myocardial Infarction; XXI Congreso Argentino de Bioingeniería; Córdoba; Argentina; 2017; 1-5CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/url/http://revista.sabi.org.ar/index.php/revista/article/view/167Nacionalinfo:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-03T09:54:59Zoai:ri.conicet.gov.ar:11336/174568instacron:CONICETInstitucionalhttp://ri.conicet.gov.ar/Organismo científico-tecnológicoNo correspondehttp://ri.conicet.gov.ar/oai/requestdasensio@conicet.gov.ar; lcarlino@conicet.gov.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:34982025-09-03 09:54:59.521CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Quaternion Neural Network with Temporal Feedback Calculation: Application to Cardiac Vector Velocity during Myocardial Infarction
title Quaternion Neural Network with Temporal Feedback Calculation: Application to Cardiac Vector Velocity during Myocardial Infarction
spellingShingle Quaternion Neural Network with Temporal Feedback Calculation: Application to Cardiac Vector Velocity during Myocardial Infarction
Cruces, Pablo Daniel
Myocardial infarction
Quaternion Algebra
Neural Networks
Cardiac vector velocity
title_short Quaternion Neural Network with Temporal Feedback Calculation: Application to Cardiac Vector Velocity during Myocardial Infarction
title_full Quaternion Neural Network with Temporal Feedback Calculation: Application to Cardiac Vector Velocity during Myocardial Infarction
title_fullStr Quaternion Neural Network with Temporal Feedback Calculation: Application to Cardiac Vector Velocity during Myocardial Infarction
title_full_unstemmed Quaternion Neural Network with Temporal Feedback Calculation: Application to Cardiac Vector Velocity during Myocardial Infarction
title_sort Quaternion Neural Network with Temporal Feedback Calculation: Application to Cardiac Vector Velocity during Myocardial Infarction
dc.creator.none.fl_str_mv Cruces, Pablo Daniel
Correa Prado, Raul Oscar
Laciar Leber, Eric
Arini, Pedro David
author Cruces, Pablo Daniel
author_facet Cruces, Pablo Daniel
Correa Prado, Raul Oscar
Laciar Leber, Eric
Arini, Pedro David
author_role author
author2 Correa Prado, Raul Oscar
Laciar Leber, Eric
Arini, Pedro David
author2_role author
author
author
dc.subject.none.fl_str_mv Myocardial infarction
Quaternion Algebra
Neural Networks
Cardiac vector velocity
topic Myocardial infarction
Quaternion Algebra
Neural Networks
Cardiac vector velocity
purl_subject.fl_str_mv https://purl.org/becyt/ford/2.2
https://purl.org/becyt/ford/2
dc.description.none.fl_txt_mv Quaternion neural networks have been shownto be useful in image and signal processing applications.Herein, we propose a novel architecture of a neural unit model characterized by its ability of encoding 3-dimensional past information and that facilitates the learning of velocity patterns. We evaluate the implementation of the networkin a study of the cardiac vector velocity and its usefulness in early detection of patients with anterior myocardial infarction. Experimental results show an improvement of the performance in terms of convergence speed and precision when comparing with traditional methods. Furthermore, the network shows successful results in measuring the velocity reduction that is usually observed in vectorcardiogram signals in the presence of myocardial damage. Through a linear discriminant analysis, a pair of 100% / 98% of sensitivity/specificity is met with only two velocity parameters. We conclude that this method is a very promising developmentfor future computational tools devoted to early diagnosis ofheart diseases.
Fil: Cruces, Pablo Daniel. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Saavedra 15. Instituto Argentino de Matemática Alberto Calderón; Argentina
Fil: Correa Prado, Raul Oscar. Universidad Nacional de San Juan. Facultad de Ingeniería. Departamento de Electrónica y Automática. Gabinete de Tecnología Médica; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Laciar Leber, Eric. Universidad Nacional de San Juan. Facultad de Ingeniería. Departamento de Electrónica y Automática. Gabinete de Tecnología Médica; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Arini, Pedro David. Universidad de Buenos Aires. Facultad de Ingeniería. Instituto de Ingeniería Biomédica; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Saavedra 15. Instituto Argentino de Matemática Alberto Calderón; Argentina
XXI Congreso Argentino de Bioingeniería
Córdoba
Argentina
Sociedad Argentina de Bioingeniería
Universidad Nacional de Córdoba
description Quaternion neural networks have been shownto be useful in image and signal processing applications.Herein, we propose a novel architecture of a neural unit model characterized by its ability of encoding 3-dimensional past information and that facilitates the learning of velocity patterns. We evaluate the implementation of the networkin a study of the cardiac vector velocity and its usefulness in early detection of patients with anterior myocardial infarction. Experimental results show an improvement of the performance in terms of convergence speed and precision when comparing with traditional methods. Furthermore, the network shows successful results in measuring the velocity reduction that is usually observed in vectorcardiogram signals in the presence of myocardial damage. Through a linear discriminant analysis, a pair of 100% / 98% of sensitivity/specificity is met with only two velocity parameters. We conclude that this method is a very promising developmentfor future computational tools devoted to early diagnosis ofheart diseases.
publishDate 2017
dc.date.none.fl_str_mv 2017
dc.type.none.fl_str_mv info:eu-repo/semantics/publishedVersion
info:eu-repo/semantics/conferenceObject
Congreso
Journal
http://purl.org/coar/resource_type/c_5794
info:ar-repo/semantics/documentoDeConferencia
status_str publishedVersion
format conferenceObject
dc.identifier.none.fl_str_mv http://hdl.handle.net/11336/174568
Quaternion Neural Network with Temporal Feedback Calculation: Application to Cardiac Vector Velocity during Myocardial Infarction; XXI Congreso Argentino de Bioingeniería; Córdoba; Argentina; 2017; 1-5
CONICET Digital
CONICET
url http://hdl.handle.net/11336/174568
identifier_str_mv Quaternion Neural Network with Temporal Feedback Calculation: Application to Cardiac Vector Velocity during Myocardial Infarction; XXI Congreso Argentino de Bioingeniería; Córdoba; Argentina; 2017; 1-5
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/url/http://revista.sabi.org.ar/index.php/revista/article/view/167
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
dc.format.none.fl_str_mv application/pdf
application/pdf
application/pdf
application/pdf
dc.coverage.none.fl_str_mv Nacional
dc.publisher.none.fl_str_mv Sociedad Argentina de Bioingeniería
publisher.none.fl_str_mv Sociedad Argentina de Bioingeniería
dc.source.none.fl_str_mv reponame:CONICET Digital (CONICET)
instname:Consejo Nacional de Investigaciones Científicas y Técnicas
reponame_str CONICET Digital (CONICET)
collection CONICET Digital (CONICET)
instname_str Consejo Nacional de Investigaciones Científicas y Técnicas
repository.name.fl_str_mv CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicas
repository.mail.fl_str_mv dasensio@conicet.gov.ar; lcarlino@conicet.gov.ar
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score 13.13397

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