Poincaré plot analysis of cerebral blood flow signals : feature extraction and classification methods for apnea detection

Objective: Rheoencephalography is a simple and inexpensive technique for cerebral blood flow assessment, however, it is not used in clinical practice since its correlation to clinical conditions has not yet been extensively proved. The present study investigates the ability of Poincaré Plot descript...

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Detalles Bibliográficos
Autores: González Pijuán, Carmen|||0000-0002-8584-8531, Jensen, Erik Weber, Gambus, Pedro L., Vallverdú Ferrer, Montserrat|||0000-0002-2031-3261
Tipo de recurso: artículo
Fecha de publicación:2018
País:España
Institución:Universitat Politècnica de Catalunya (UPC)
Repositorio:UPCommons. Portal del coneixement obert de la UPC
Idioma:inglés
OAI Identifier:oai:upcommons.upc.edu:2117/127011
Acceso en línea:https://hdl.handle.net/2117/127011
https://dx.doi.org/10.1371/journal.pone.0208642
Access Level:acceso abierto
Palabra clave:Rheoencephalography
Cerebral circulation
Apnea
Circulació cerebral
Àrees temàtiques de la UPC::Informàtica::Aplicacions de la informàtica::Bioinformàtica
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oai_identifier_str oai:upcommons.upc.edu:2117/127011
network_acronym_str ES
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repository_id_str
spelling Poincaré plot analysis of cerebral blood flow signals : feature extraction and classification methods for apnea detectionGonzález Pijuán, Carmen|||0000-0002-8584-8531Jensen, Erik WeberGambus, Pedro L.Vallverdú Ferrer, Montserrat|||0000-0002-2031-3261RheoencephalographyCerebral circulationApneaCirculació cerebralÀrees temàtiques de la UPC::Informàtica::Aplicacions de la informàtica::BioinformàticaObjective: Rheoencephalography is a simple and inexpensive technique for cerebral blood flow assessment, however, it is not used in clinical practice since its correlation to clinical conditions has not yet been extensively proved. The present study investigates the ability of Poincaré Plot descriptors from rheoencephalography signals to detect apneas in volunteers. Methods:A group of 16 subjects participated in the study. Rheoencephalography data from baseline and apnea periods were recorded and Poincaré Plot descriptors were extracted from the reconstructed attractors with different time lags (t). Among the set of extracted features, those presenting significant differences between baseline and apnea recordings were used as inputs to four different classifiers to optimize the apnea detection. Results:Three features showed significant differences between apnea and baseline signals: the Poincaré Plot ratio (SDratio), its correlation (R) and the Complex Correlation Measure (CCM). Those differences were optimized for time lags smaller than those recommended in previous works for other biomedical signals, all of them being lower than the threshold established by the position of the inflection point in the CCM curves. The classifier showing the best performance was the classification tree, with 81% accuracy and an area under the curve of the receiver operating characteristic of 0.927. This performance was obtained using a single input parameter, either SDratio or R. Conclusions Poincaré Plot features extracted from the attractors of rheoencephalographic signals were able to track cerebral blood flow changes provoked by breath holding. Even though further validation with independent datasets is needed, those results suggest that nonlinear analysis of rheoencephalography might be a useful approach to assess the correlation of cerebral impedance with clinical changesPeer ReviewedPublic Library of Science (PLOS)20182018-12-0720192019-01-17journal articlehttp://purl.org/coar/resource_type/c_6501VoRhttp://purl.org/coar/version/c_970fb48d4fbd8a85info:eu-repo/semantics/articleapplication/pdfhttps://hdl.handle.net/2117/127011https://dx.doi.org/10.1371/journal.pone.0208642reponame:UPCommons. Portal del coneixement obert de la UPCinstname:Universitat Politècnica de Catalunya (UPC)Inglésengopen accesshttp://purl.org/coar/access_right/c_abf2Attribution 3.0 Spainhttp://creativecommons.org/licenses/by/3.0/es/info:eu-repo/semantics/openAccessoai:upcommons.upc.edu:2117/1270112026-05-27T15:37:01Z
dc.title.none.fl_str_mv Poincaré plot analysis of cerebral blood flow signals : feature extraction and classification methods for apnea detection
title Poincaré plot analysis of cerebral blood flow signals : feature extraction and classification methods for apnea detection
spellingShingle Poincaré plot analysis of cerebral blood flow signals : feature extraction and classification methods for apnea detection
González Pijuán, Carmen|||0000-0002-8584-8531
Rheoencephalography
Cerebral circulation
Apnea
Circulació cerebral
Àrees temàtiques de la UPC::Informàtica::Aplicacions de la informàtica::Bioinformàtica
title_short Poincaré plot analysis of cerebral blood flow signals : feature extraction and classification methods for apnea detection
title_full Poincaré plot analysis of cerebral blood flow signals : feature extraction and classification methods for apnea detection
title_fullStr Poincaré plot analysis of cerebral blood flow signals : feature extraction and classification methods for apnea detection
title_full_unstemmed Poincaré plot analysis of cerebral blood flow signals : feature extraction and classification methods for apnea detection
title_sort Poincaré plot analysis of cerebral blood flow signals : feature extraction and classification methods for apnea detection
dc.creator.none.fl_str_mv González Pijuán, Carmen|||0000-0002-8584-8531
Jensen, Erik Weber
Gambus, Pedro L.
Vallverdú Ferrer, Montserrat|||0000-0002-2031-3261
author González Pijuán, Carmen|||0000-0002-8584-8531
author_facet González Pijuán, Carmen|||0000-0002-8584-8531
Jensen, Erik Weber
Gambus, Pedro L.
Vallverdú Ferrer, Montserrat|||0000-0002-2031-3261
author_role author
author2 Jensen, Erik Weber
Gambus, Pedro L.
Vallverdú Ferrer, Montserrat|||0000-0002-2031-3261
author2_role author
author
author
dc.subject.none.fl_str_mv Rheoencephalography
Cerebral circulation
Apnea
Circulació cerebral
Àrees temàtiques de la UPC::Informàtica::Aplicacions de la informàtica::Bioinformàtica
topic Rheoencephalography
Cerebral circulation
Apnea
Circulació cerebral
Àrees temàtiques de la UPC::Informàtica::Aplicacions de la informàtica::Bioinformàtica
description Objective: Rheoencephalography is a simple and inexpensive technique for cerebral blood flow assessment, however, it is not used in clinical practice since its correlation to clinical conditions has not yet been extensively proved. The present study investigates the ability of Poincaré Plot descriptors from rheoencephalography signals to detect apneas in volunteers. Methods:A group of 16 subjects participated in the study. Rheoencephalography data from baseline and apnea periods were recorded and Poincaré Plot descriptors were extracted from the reconstructed attractors with different time lags (t). Among the set of extracted features, those presenting significant differences between baseline and apnea recordings were used as inputs to four different classifiers to optimize the apnea detection. Results:Three features showed significant differences between apnea and baseline signals: the Poincaré Plot ratio (SDratio), its correlation (R) and the Complex Correlation Measure (CCM). Those differences were optimized for time lags smaller than those recommended in previous works for other biomedical signals, all of them being lower than the threshold established by the position of the inflection point in the CCM curves. The classifier showing the best performance was the classification tree, with 81% accuracy and an area under the curve of the receiver operating characteristic of 0.927. This performance was obtained using a single input parameter, either SDratio or R. Conclusions Poincaré Plot features extracted from the attractors of rheoencephalographic signals were able to track cerebral blood flow changes provoked by breath holding. Even though further validation with independent datasets is needed, those results suggest that nonlinear analysis of rheoencephalography might be a useful approach to assess the correlation of cerebral impedance with clinical changes
publishDate 2018
dc.date.none.fl_str_mv 2018
2018-12-07
2019
2019-01-17
dc.type.none.fl_str_mv journal article
http://purl.org/coar/resource_type/c_6501
VoR
http://purl.org/coar/version/c_970fb48d4fbd8a85
dc.type.openaire.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv https://hdl.handle.net/2117/127011
https://dx.doi.org/10.1371/journal.pone.0208642
url https://hdl.handle.net/2117/127011
https://dx.doi.org/10.1371/journal.pone.0208642
dc.language.none.fl_str_mv Inglés
eng
language_invalid_str_mv Inglés
language eng
dc.rights.none.fl_str_mv open access
http://purl.org/coar/access_right/c_abf2
Attribution 3.0 Spain
http://creativecommons.org/licenses/by/3.0/es/
dc.rights.openaire.fl_str_mv info:eu-repo/semantics/openAccess
rights_invalid_str_mv open access
http://purl.org/coar/access_right/c_abf2
Attribution 3.0 Spain
http://creativecommons.org/licenses/by/3.0/es/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Public Library of Science (PLOS)
publisher.none.fl_str_mv Public Library of Science (PLOS)
dc.source.none.fl_str_mv reponame:UPCommons. Portal del coneixement obert de la UPC
instname:Universitat Politècnica de Catalunya (UPC)
instname_str Universitat Politècnica de Catalunya (UPC)
reponame_str UPCommons. Portal del coneixement obert de la UPC
collection UPCommons. Portal del coneixement obert de la UPC
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15,300719

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