An improved ECG-derived respiration method using kernel principal component analysis
Recent studies show that principal component analysis (PCA) of heart beats generates well-performing ECG-derived respiratory signals (EDR). This study aims at improving the performance of EDR signals using kernel PCA (kPCA). Kernel PCA is a generalization of PCA where nonlinearities in the data are...
- Autores:
- Tipo de recurso:
- Fecha de publicación:
- 2011
- Institución:
- Universidad del Rosario
- Repositorio:
- Repositorio EdocUR - U. Rosario
- Idioma:
- eng
- OAI Identifier:
- oai:repository.urosario.edu.co:10336/28434
- Acceso en línea:
- https://repository.urosario.edu.co/handle/10336/28434
- Palabra clave:
- Principal component analysis
Kernel
Electrocardiography
Correlation
Coherence
Sensors
Optimization
- Rights
- License
- Restringido (Acceso a grupos específicos)
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EDOCUR2 |
network_name_str |
Repositorio EdocUR - U. Rosario |
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1413951260026c827fb-524d-4c98-829a-5c9a9c9c6127600373c77e3-5093-43fd-831b-0db88cefa50d6007f4da012-2e74-4a5d-b3d1-6e03864b76ef6002020-08-28T15:48:11Z2020-08-28T15:48:11Z2011-09-182011Recent studies show that principal component analysis (PCA) of heart beats generates well-performing ECG-derived respiratory signals (EDR). This study aims at improving the performance of EDR signals using kernel PCA (kPCA). Kernel PCA is a generalization of PCA where nonlinearities in the data are taken into account for the decomposition. The performance of PCA and kPCA is evaluated by comparing the EDR signals to the reference respiratory signal. Correlation coefficients of 0.630 ± 0.189 and 0.675 ± 0.163, and magnitude squared coherence coefficients at respiratory frequency of 0.819 ± 0.229 and 0.894 ± 0.139 were obtained for PCA and kPCA respectively. The Wilcoxon signed rank test showed statistically significantly higher coefficients for kPCA than for PCA for both the correlation (p = 0.0257) and coherence (p = 0.0030) coefficients. To conclude, kPCA proves to outperform PCA in the extraction of a respiratory signal from single lead ECGs.application/pdfISSN: 0276-6574EISSN: 2325-8853https://repository.urosario.edu.co/handle/10336/28434engEngineering in Medicine and Biology SocietyComputing in CardiologyComputing in Cardiology,ISSN: 0276-6574; EISSN: 2325-8853 (2011)https://ieeexplore.ieee.org/abstract/document/6164498https://search-ebscohost-com.ez.urosario.edu.co/login.aspx?direct=true&AuthType=ip&db=edseee&AN=edseee.6164498&lang=es&site=eds-live&scope=siteRestringido (Acceso a grupos específicos)http://purl.org/coar/access_right/c_16ecComputing in Cardiologyinstname:Universidad del Rosarioreponame:Repositorio Institucional EdocURPrincipal component analysisKernelElectrocardiographyCorrelationCoherenceSensorsOptimizationAn improved ECG-derived respiration method using kernel principal component analysisUn método mejorado de respiración derivado de ECG que utiliza análisis de componentes principales del núcleoarticleArtículohttp://purl.org/coar/version/c_970fb48d4fbd8a85http://purl.org/coar/resource_type/c_6501Caicedo Dorado, AlexanderVaron, CarolinaVan Huffel, SabineWidjaja, Devy10336/28434oai:repository.urosario.edu.co:10336/284342022-10-06 08:49:21.798https://repository.urosario.edu.coRepositorio institucional EdocURedocur@urosario.edu.co |
dc.title.spa.fl_str_mv |
An improved ECG-derived respiration method using kernel principal component analysis |
dc.title.TranslatedTitle.spa.fl_str_mv |
Un método mejorado de respiración derivado de ECG que utiliza análisis de componentes principales del núcleo |
title |
An improved ECG-derived respiration method using kernel principal component analysis |
spellingShingle |
An improved ECG-derived respiration method using kernel principal component analysis Principal component analysis Kernel Electrocardiography Correlation Coherence Sensors Optimization |
title_short |
An improved ECG-derived respiration method using kernel principal component analysis |
title_full |
An improved ECG-derived respiration method using kernel principal component analysis |
title_fullStr |
An improved ECG-derived respiration method using kernel principal component analysis |
title_full_unstemmed |
An improved ECG-derived respiration method using kernel principal component analysis |
title_sort |
An improved ECG-derived respiration method using kernel principal component analysis |
dc.subject.keyword.spa.fl_str_mv |
Principal component analysis Kernel Electrocardiography Correlation Coherence Sensors Optimization |
topic |
Principal component analysis Kernel Electrocardiography Correlation Coherence Sensors Optimization |
description |
Recent studies show that principal component analysis (PCA) of heart beats generates well-performing ECG-derived respiratory signals (EDR). This study aims at improving the performance of EDR signals using kernel PCA (kPCA). Kernel PCA is a generalization of PCA where nonlinearities in the data are taken into account for the decomposition. The performance of PCA and kPCA is evaluated by comparing the EDR signals to the reference respiratory signal. Correlation coefficients of 0.630 ± 0.189 and 0.675 ± 0.163, and magnitude squared coherence coefficients at respiratory frequency of 0.819 ± 0.229 and 0.894 ± 0.139 were obtained for PCA and kPCA respectively. The Wilcoxon signed rank test showed statistically significantly higher coefficients for kPCA than for PCA for both the correlation (p = 0.0257) and coherence (p = 0.0030) coefficients. To conclude, kPCA proves to outperform PCA in the extraction of a respiratory signal from single lead ECGs. |
publishDate |
2011 |
dc.date.created.spa.fl_str_mv |
2011-09-18 |
dc.date.issued.none.fl_str_mv |
2011 |
dc.date.accessioned.none.fl_str_mv |
2020-08-28T15:48:11Z |
dc.date.available.none.fl_str_mv |
2020-08-28T15:48:11Z |
dc.type.eng.fl_str_mv |
article |
dc.type.coarversion.fl_str_mv |
http://purl.org/coar/version/c_970fb48d4fbd8a85 |
dc.type.coar.fl_str_mv |
http://purl.org/coar/resource_type/c_6501 |
dc.type.spa.spa.fl_str_mv |
Artículo |
dc.identifier.issn.none.fl_str_mv |
ISSN: 0276-6574 EISSN: 2325-8853 |
dc.identifier.uri.none.fl_str_mv |
https://repository.urosario.edu.co/handle/10336/28434 |
identifier_str_mv |
ISSN: 0276-6574 EISSN: 2325-8853 |
url |
https://repository.urosario.edu.co/handle/10336/28434 |
dc.language.iso.spa.fl_str_mv |
eng |
language |
eng |
dc.relation.citationTitle.none.fl_str_mv |
Computing in Cardiology |
dc.relation.ispartof.spa.fl_str_mv |
Computing in Cardiology,ISSN: 0276-6574; EISSN: 2325-8853 (2011) |
dc.relation.uri.spa.fl_str_mv |
https://ieeexplore.ieee.org/abstract/document/6164498 |
dc.relation.uri.none.fl_str_mv |
https://search-ebscohost-com.ez.urosario.edu.co/login.aspx?direct=true&AuthType=ip&db=edseee&AN=edseee.6164498&lang=es&site=eds-live&scope=site |
dc.rights.coar.fl_str_mv |
http://purl.org/coar/access_right/c_16ec |
dc.rights.acceso.spa.fl_str_mv |
Restringido (Acceso a grupos específicos) |
rights_invalid_str_mv |
Restringido (Acceso a grupos específicos) http://purl.org/coar/access_right/c_16ec |
dc.format.mimetype.none.fl_str_mv |
application/pdf |
dc.publisher.spa.fl_str_mv |
Engineering in Medicine and Biology Society |
dc.source.spa.fl_str_mv |
Computing in Cardiology |
institution |
Universidad del Rosario |
dc.source.instname.none.fl_str_mv |
instname:Universidad del Rosario |
dc.source.reponame.none.fl_str_mv |
reponame:Repositorio Institucional EdocUR |
repository.name.fl_str_mv |
Repositorio institucional EdocUR |
repository.mail.fl_str_mv |
edocur@urosario.edu.co |
_version_ |
1814167610900611072 |