A snoring classifier based on heart rate variability analysis
Autor(a) principal: | |
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Data de Publicação: | 2011 |
Outros Autores: | , , , , , , , |
Tipo de documento: | Artigo de conferência |
Idioma: | eng |
Título da fonte: | Repositório Institucional da UNESP |
Texto Completo: | http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=6164573 http://hdl.handle.net/11449/72936 |
Resumo: | The effect of snoring on the cardiovascular system is not well-known. In this study we analyzed the Heart Rate Variability (HRV) differences between light and heavy snorers. The experiments are done on the full-whole-night polysomnography (PSG) with ECG and audio channels from patient group (heavy snorer) and control group (light snorer), which are gender- and age-paired, totally 30 subjects. A feature Snoring Density (SND) of audio signal as classification criterion and HRV features are computed. Mann-Whitney statistical test and Support Vector Machine (SVM) classification are done to see the correlation. The result of this study shows that snoring has close impact on the HRV features. This result can provide a deeper insight into the physiological understand of snoring. © 2011 CCAL. |
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Repositório Institucional da UNESP |
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A snoring classifier based on heart rate variability analysisAudio channelsAudio signalClassification criterionControl groupsHeart rate variabilityMann-WhitneyPolysomnographySupport vector machine (SVM)CardiologyHeartStatistical testsSupport vector machinesThe effect of snoring on the cardiovascular system is not well-known. In this study we analyzed the Heart Rate Variability (HRV) differences between light and heavy snorers. The experiments are done on the full-whole-night polysomnography (PSG) with ECG and audio channels from patient group (heavy snorer) and control group (light snorer), which are gender- and age-paired, totally 30 subjects. A feature Snoring Density (SND) of audio signal as classification criterion and HRV features are computed. Mann-Whitney statistical test and Support Vector Machine (SVM) classification are done to see the correlation. The result of this study shows that snoring has close impact on the HRV features. This result can provide a deeper insight into the physiological understand of snoring. © 2011 CCAL.Department of Electrical and Computer Engineering University of MacauEvolutionary Systems and Biomedical Engineering Lab. Technical University of LisbonDepartment of Neurology UNESP, BotucatuDepartment of Neurology UNESP, BotucatuUniversity of MacauTechnical University of LisbonUniversidade Estadual Paulista (Unesp)Ieong, Chio-InDong, ChengNan, WenyaRosa, AgostinhoGuimarães, Ronaldo [UNESP]Vai, Mang-I.Mak, Pui-InWan, FengMak, Peng-Un2014-05-27T11:26:16Z2014-05-27T11:26:16Z2011-12-01info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/conferenceObject345-348http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=6164573Computing in Cardiology, v. 38, p. 345-348.2325-88612325-887Xhttp://hdl.handle.net/11449/729362-s2.0-84859963132Scopusreponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPengComputing in Cardiology0,191info:eu-repo/semantics/openAccess2024-08-16T15:46:44Zoai:repositorio.unesp.br:11449/72936Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462024-08-16T15:46:44Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false |
dc.title.none.fl_str_mv |
A snoring classifier based on heart rate variability analysis |
title |
A snoring classifier based on heart rate variability analysis |
spellingShingle |
A snoring classifier based on heart rate variability analysis Ieong, Chio-In Audio channels Audio signal Classification criterion Control groups Heart rate variability Mann-Whitney Polysomnography Support vector machine (SVM) Cardiology Heart Statistical tests Support vector machines |
title_short |
A snoring classifier based on heart rate variability analysis |
title_full |
A snoring classifier based on heart rate variability analysis |
title_fullStr |
A snoring classifier based on heart rate variability analysis |
title_full_unstemmed |
A snoring classifier based on heart rate variability analysis |
title_sort |
A snoring classifier based on heart rate variability analysis |
author |
Ieong, Chio-In |
author_facet |
Ieong, Chio-In Dong, Cheng Nan, Wenya Rosa, Agostinho Guimarães, Ronaldo [UNESP] Vai, Mang-I. Mak, Pui-In Wan, Feng Mak, Peng-Un |
author_role |
author |
author2 |
Dong, Cheng Nan, Wenya Rosa, Agostinho Guimarães, Ronaldo [UNESP] Vai, Mang-I. Mak, Pui-In Wan, Feng Mak, Peng-Un |
author2_role |
author author author author author author author author |
dc.contributor.none.fl_str_mv |
University of Macau Technical University of Lisbon Universidade Estadual Paulista (Unesp) |
dc.contributor.author.fl_str_mv |
Ieong, Chio-In Dong, Cheng Nan, Wenya Rosa, Agostinho Guimarães, Ronaldo [UNESP] Vai, Mang-I. Mak, Pui-In Wan, Feng Mak, Peng-Un |
dc.subject.por.fl_str_mv |
Audio channels Audio signal Classification criterion Control groups Heart rate variability Mann-Whitney Polysomnography Support vector machine (SVM) Cardiology Heart Statistical tests Support vector machines |
topic |
Audio channels Audio signal Classification criterion Control groups Heart rate variability Mann-Whitney Polysomnography Support vector machine (SVM) Cardiology Heart Statistical tests Support vector machines |
description |
The effect of snoring on the cardiovascular system is not well-known. In this study we analyzed the Heart Rate Variability (HRV) differences between light and heavy snorers. The experiments are done on the full-whole-night polysomnography (PSG) with ECG and audio channels from patient group (heavy snorer) and control group (light snorer), which are gender- and age-paired, totally 30 subjects. A feature Snoring Density (SND) of audio signal as classification criterion and HRV features are computed. Mann-Whitney statistical test and Support Vector Machine (SVM) classification are done to see the correlation. The result of this study shows that snoring has close impact on the HRV features. This result can provide a deeper insight into the physiological understand of snoring. © 2011 CCAL. |
publishDate |
2011 |
dc.date.none.fl_str_mv |
2011-12-01 2014-05-27T11:26:16Z 2014-05-27T11:26:16Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/conferenceObject |
format |
conferenceObject |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=6164573 Computing in Cardiology, v. 38, p. 345-348. 2325-8861 2325-887X http://hdl.handle.net/11449/72936 2-s2.0-84859963132 |
url |
http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=6164573 http://hdl.handle.net/11449/72936 |
identifier_str_mv |
Computing in Cardiology, v. 38, p. 345-348. 2325-8861 2325-887X 2-s2.0-84859963132 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
Computing in Cardiology 0,191 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
345-348 |
dc.source.none.fl_str_mv |
Scopus reponame:Repositório Institucional da UNESP instname:Universidade Estadual Paulista (UNESP) instacron:UNESP |
instname_str |
Universidade Estadual Paulista (UNESP) |
instacron_str |
UNESP |
institution |
UNESP |
reponame_str |
Repositório Institucional da UNESP |
collection |
Repositório Institucional da UNESP |
repository.name.fl_str_mv |
Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP) |
repository.mail.fl_str_mv |
|
_version_ |
1808128198220709888 |