Selection of Nonstationary Dynamic Features for Obstructive Sleep Apnoea Detection in Children

Abstract This paper discusses the methodology for selecting a set of relevant nonstationary features to increase the specificity of the obstructive sleep apnea detector. Dynamic features are extracted from time-evolving spectral representation of photoplethysmography envelope recordings. In this reg...
Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Sepulveda-Cano, L. M. [verfasserIn] Gil, E. [verfasserIn] Laguna, P. [verfasserIn] Castellanos-Dominguez, G. [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2011

Schlagwörter:	Obstructive Sleep Apnea Sleep Apnea Dynamic Feature Obstructive Sleep Apnoea Polysomnography Recording

Übergeordnetes Werk:	Enthalten in: EURASIP journal on advances in signal processing - Heidelberg : Springer, 2007, 2011(2011), 1 vom: 21. Feb.
Übergeordnetes Werk:	volume:2011 ; year:2011 ; number:1 ; day:21 ; month:02

Links:	Volltext

DOI / URN:	10.1155/2011/538314

Katalog-ID:	SPR031997813

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allfields	10.1155/2011/538314 doi (DE-627)SPR031997813 (SPR)538314-e DE-627 ger DE-627 rakwb eng 620 ASE 53.73 bkl Sepulveda-Cano, L. M. verfasserin aut Selection of Nonstationary Dynamic Features for Obstructive Sleep Apnoea Detection in Children 2011 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract This paper discusses the methodology for selecting a set of relevant nonstationary features to increase the specificity of the obstructive sleep apnea detector. Dynamic features are extracted from time-evolving spectral representation of photoplethysmography envelope recordings. In this regard, a time-evolving version of the standard linear multivariate decomposition is discussed to perform stochastic dimensionality reduction. For training aim, this work analyzes the concrete set comprising filter banked dynamic features that include spectral centroids, the cepstral coefficients as well as their time-variant energies. Performance of classifier accuracy is provided for the collected polysomnography recordings of 21 children. Moreover, since the apnea diagnosing is based on analysis of set of fragments partitioned from the photoplethysmography envelope recordings, a new approach for their indirect labeling is described. As a result, performed outcomes of accuracy bring enough evidence that if using a subset of cepstral-based dynamic features, then patient classification accuracy can reach as much as 83.3% value, when using a k-nn classifier, as well. Therefore, photoplethysmography-based detection provides an adequate scheme for obstructive sleep apnea diagnosis. Obstructive Sleep Apnea (dpeaa)DE-He213 Sleep Apnea (dpeaa)DE-He213 Dynamic Feature (dpeaa)DE-He213 Obstructive Sleep Apnoea (dpeaa)DE-He213 Polysomnography Recording (dpeaa)DE-He213 Gil, E. verfasserin aut Laguna, P. verfasserin aut Castellanos-Dominguez, G. verfasserin aut Enthalten in EURASIP journal on advances in signal processing Heidelberg : Springer, 2007 2011(2011), 1 vom: 21. Feb. (DE-627)534054277 (DE-600)2364203-8 1687-6180 nnns volume:2011 year:2011 number:1 day:21 month:02 https://dx.doi.org/10.1155/2011/538314 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_2522 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 53.73 ASE AR 2011 2011 1 21 02
spelling	10.1155/2011/538314 doi (DE-627)SPR031997813 (SPR)538314-e DE-627 ger DE-627 rakwb eng 620 ASE 53.73 bkl Sepulveda-Cano, L. M. verfasserin aut Selection of Nonstationary Dynamic Features for Obstructive Sleep Apnoea Detection in Children 2011 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract This paper discusses the methodology for selecting a set of relevant nonstationary features to increase the specificity of the obstructive sleep apnea detector. Dynamic features are extracted from time-evolving spectral representation of photoplethysmography envelope recordings. In this regard, a time-evolving version of the standard linear multivariate decomposition is discussed to perform stochastic dimensionality reduction. For training aim, this work analyzes the concrete set comprising filter banked dynamic features that include spectral centroids, the cepstral coefficients as well as their time-variant energies. Performance of classifier accuracy is provided for the collected polysomnography recordings of 21 children. Moreover, since the apnea diagnosing is based on analysis of set of fragments partitioned from the photoplethysmography envelope recordings, a new approach for their indirect labeling is described. As a result, performed outcomes of accuracy bring enough evidence that if using a subset of cepstral-based dynamic features, then patient classification accuracy can reach as much as 83.3% value, when using a k-nn classifier, as well. Therefore, photoplethysmography-based detection provides an adequate scheme for obstructive sleep apnea diagnosis. Obstructive Sleep Apnea (dpeaa)DE-He213 Sleep Apnea (dpeaa)DE-He213 Dynamic Feature (dpeaa)DE-He213 Obstructive Sleep Apnoea (dpeaa)DE-He213 Polysomnography Recording (dpeaa)DE-He213 Gil, E. verfasserin aut Laguna, P. verfasserin aut Castellanos-Dominguez, G. verfasserin aut Enthalten in EURASIP journal on advances in signal processing Heidelberg : Springer, 2007 2011(2011), 1 vom: 21. Feb. (DE-627)534054277 (DE-600)2364203-8 1687-6180 nnns volume:2011 year:2011 number:1 day:21 month:02 https://dx.doi.org/10.1155/2011/538314 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_2522 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 53.73 ASE AR 2011 2011 1 21 02
allfields_unstemmed	10.1155/2011/538314 doi (DE-627)SPR031997813 (SPR)538314-e DE-627 ger DE-627 rakwb eng 620 ASE 53.73 bkl Sepulveda-Cano, L. M. verfasserin aut Selection of Nonstationary Dynamic Features for Obstructive Sleep Apnoea Detection in Children 2011 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract This paper discusses the methodology for selecting a set of relevant nonstationary features to increase the specificity of the obstructive sleep apnea detector. Dynamic features are extracted from time-evolving spectral representation of photoplethysmography envelope recordings. In this regard, a time-evolving version of the standard linear multivariate decomposition is discussed to perform stochastic dimensionality reduction. For training aim, this work analyzes the concrete set comprising filter banked dynamic features that include spectral centroids, the cepstral coefficients as well as their time-variant energies. Performance of classifier accuracy is provided for the collected polysomnography recordings of 21 children. Moreover, since the apnea diagnosing is based on analysis of set of fragments partitioned from the photoplethysmography envelope recordings, a new approach for their indirect labeling is described. As a result, performed outcomes of accuracy bring enough evidence that if using a subset of cepstral-based dynamic features, then patient classification accuracy can reach as much as 83.3% value, when using a k-nn classifier, as well. Therefore, photoplethysmography-based detection provides an adequate scheme for obstructive sleep apnea diagnosis. Obstructive Sleep Apnea (dpeaa)DE-He213 Sleep Apnea (dpeaa)DE-He213 Dynamic Feature (dpeaa)DE-He213 Obstructive Sleep Apnoea (dpeaa)DE-He213 Polysomnography Recording (dpeaa)DE-He213 Gil, E. verfasserin aut Laguna, P. verfasserin aut Castellanos-Dominguez, G. verfasserin aut Enthalten in EURASIP journal on advances in signal processing Heidelberg : Springer, 2007 2011(2011), 1 vom: 21. Feb. (DE-627)534054277 (DE-600)2364203-8 1687-6180 nnns volume:2011 year:2011 number:1 day:21 month:02 https://dx.doi.org/10.1155/2011/538314 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_2522 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 53.73 ASE AR 2011 2011 1 21 02
allfieldsGer	10.1155/2011/538314 doi (DE-627)SPR031997813 (SPR)538314-e DE-627 ger DE-627 rakwb eng 620 ASE 53.73 bkl Sepulveda-Cano, L. M. verfasserin aut Selection of Nonstationary Dynamic Features for Obstructive Sleep Apnoea Detection in Children 2011 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract This paper discusses the methodology for selecting a set of relevant nonstationary features to increase the specificity of the obstructive sleep apnea detector. Dynamic features are extracted from time-evolving spectral representation of photoplethysmography envelope recordings. In this regard, a time-evolving version of the standard linear multivariate decomposition is discussed to perform stochastic dimensionality reduction. For training aim, this work analyzes the concrete set comprising filter banked dynamic features that include spectral centroids, the cepstral coefficients as well as their time-variant energies. Performance of classifier accuracy is provided for the collected polysomnography recordings of 21 children. Moreover, since the apnea diagnosing is based on analysis of set of fragments partitioned from the photoplethysmography envelope recordings, a new approach for their indirect labeling is described. As a result, performed outcomes of accuracy bring enough evidence that if using a subset of cepstral-based dynamic features, then patient classification accuracy can reach as much as 83.3% value, when using a k-nn classifier, as well. Therefore, photoplethysmography-based detection provides an adequate scheme for obstructive sleep apnea diagnosis. Obstructive Sleep Apnea (dpeaa)DE-He213 Sleep Apnea (dpeaa)DE-He213 Dynamic Feature (dpeaa)DE-He213 Obstructive Sleep Apnoea (dpeaa)DE-He213 Polysomnography Recording (dpeaa)DE-He213 Gil, E. verfasserin aut Laguna, P. verfasserin aut Castellanos-Dominguez, G. verfasserin aut Enthalten in EURASIP journal on advances in signal processing Heidelberg : Springer, 2007 2011(2011), 1 vom: 21. Feb. (DE-627)534054277 (DE-600)2364203-8 1687-6180 nnns volume:2011 year:2011 number:1 day:21 month:02 https://dx.doi.org/10.1155/2011/538314 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_2522 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 53.73 ASE AR 2011 2011 1 21 02
allfieldsSound	10.1155/2011/538314 doi (DE-627)SPR031997813 (SPR)538314-e DE-627 ger DE-627 rakwb eng 620 ASE 53.73 bkl Sepulveda-Cano, L. M. verfasserin aut Selection of Nonstationary Dynamic Features for Obstructive Sleep Apnoea Detection in Children 2011 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract This paper discusses the methodology for selecting a set of relevant nonstationary features to increase the specificity of the obstructive sleep apnea detector. Dynamic features are extracted from time-evolving spectral representation of photoplethysmography envelope recordings. In this regard, a time-evolving version of the standard linear multivariate decomposition is discussed to perform stochastic dimensionality reduction. For training aim, this work analyzes the concrete set comprising filter banked dynamic features that include spectral centroids, the cepstral coefficients as well as their time-variant energies. Performance of classifier accuracy is provided for the collected polysomnography recordings of 21 children. Moreover, since the apnea diagnosing is based on analysis of set of fragments partitioned from the photoplethysmography envelope recordings, a new approach for their indirect labeling is described. As a result, performed outcomes of accuracy bring enough evidence that if using a subset of cepstral-based dynamic features, then patient classification accuracy can reach as much as 83.3% value, when using a k-nn classifier, as well. Therefore, photoplethysmography-based detection provides an adequate scheme for obstructive sleep apnea diagnosis. Obstructive Sleep Apnea (dpeaa)DE-He213 Sleep Apnea (dpeaa)DE-He213 Dynamic Feature (dpeaa)DE-He213 Obstructive Sleep Apnoea (dpeaa)DE-He213 Polysomnography Recording (dpeaa)DE-He213 Gil, E. verfasserin aut Laguna, P. verfasserin aut Castellanos-Dominguez, G. verfasserin aut Enthalten in EURASIP journal on advances in signal processing Heidelberg : Springer, 2007 2011(2011), 1 vom: 21. Feb. (DE-627)534054277 (DE-600)2364203-8 1687-6180 nnns volume:2011 year:2011 number:1 day:21 month:02 https://dx.doi.org/10.1155/2011/538314 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_2522 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 53.73 ASE AR 2011 2011 1 21 02
language	English
source	Enthalten in EURASIP journal on advances in signal processing 2011(2011), 1 vom: 21. Feb. volume:2011 year:2011 number:1 day:21 month:02
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topic_facet	Obstructive Sleep Apnea Sleep Apnea Dynamic Feature Obstructive Sleep Apnoea Polysomnography Recording
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M.</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Selection of Nonstationary Dynamic Features for Obstructive Sleep Apnoea Detection in Children</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2011</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Abstract This paper discusses the methodology for selecting a set of relevant nonstationary features to increase the specificity of the obstructive sleep apnea detector. 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author	Sepulveda-Cano, L. M.
spellingShingle	Sepulveda-Cano, L. M. ddc 620 bkl 53.73 misc Obstructive Sleep Apnea misc Sleep Apnea misc Dynamic Feature misc Obstructive Sleep Apnoea misc Polysomnography Recording Selection of Nonstationary Dynamic Features for Obstructive Sleep Apnoea Detection in Children
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topic_title	620 ASE 53.73 bkl Selection of Nonstationary Dynamic Features for Obstructive Sleep Apnoea Detection in Children Obstructive Sleep Apnea (dpeaa)DE-He213 Sleep Apnea (dpeaa)DE-He213 Dynamic Feature (dpeaa)DE-He213 Obstructive Sleep Apnoea (dpeaa)DE-He213 Polysomnography Recording (dpeaa)DE-He213
topic	ddc 620 bkl 53.73 misc Obstructive Sleep Apnea misc Sleep Apnea misc Dynamic Feature misc Obstructive Sleep Apnoea misc Polysomnography Recording
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title	Selection of Nonstationary Dynamic Features for Obstructive Sleep Apnoea Detection in Children
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title_full	Selection of Nonstationary Dynamic Features for Obstructive Sleep Apnoea Detection in Children
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title_sort	selection of nonstationary dynamic features for obstructive sleep apnoea detection in children
title_auth	Selection of Nonstationary Dynamic Features for Obstructive Sleep Apnoea Detection in Children
abstract	Abstract This paper discusses the methodology for selecting a set of relevant nonstationary features to increase the specificity of the obstructive sleep apnea detector. Dynamic features are extracted from time-evolving spectral representation of photoplethysmography envelope recordings. In this regard, a time-evolving version of the standard linear multivariate decomposition is discussed to perform stochastic dimensionality reduction. For training aim, this work analyzes the concrete set comprising filter banked dynamic features that include spectral centroids, the cepstral coefficients as well as their time-variant energies. Performance of classifier accuracy is provided for the collected polysomnography recordings of 21 children. Moreover, since the apnea diagnosing is based on analysis of set of fragments partitioned from the photoplethysmography envelope recordings, a new approach for their indirect labeling is described. As a result, performed outcomes of accuracy bring enough evidence that if using a subset of cepstral-based dynamic features, then patient classification accuracy can reach as much as 83.3% value, when using a k-nn classifier, as well. Therefore, photoplethysmography-based detection provides an adequate scheme for obstructive sleep apnea diagnosis.
abstractGer	Abstract This paper discusses the methodology for selecting a set of relevant nonstationary features to increase the specificity of the obstructive sleep apnea detector. Dynamic features are extracted from time-evolving spectral representation of photoplethysmography envelope recordings. In this regard, a time-evolving version of the standard linear multivariate decomposition is discussed to perform stochastic dimensionality reduction. For training aim, this work analyzes the concrete set comprising filter banked dynamic features that include spectral centroids, the cepstral coefficients as well as their time-variant energies. Performance of classifier accuracy is provided for the collected polysomnography recordings of 21 children. Moreover, since the apnea diagnosing is based on analysis of set of fragments partitioned from the photoplethysmography envelope recordings, a new approach for their indirect labeling is described. As a result, performed outcomes of accuracy bring enough evidence that if using a subset of cepstral-based dynamic features, then patient classification accuracy can reach as much as 83.3% value, when using a k-nn classifier, as well. Therefore, photoplethysmography-based detection provides an adequate scheme for obstructive sleep apnea diagnosis.
abstract_unstemmed	Abstract This paper discusses the methodology for selecting a set of relevant nonstationary features to increase the specificity of the obstructive sleep apnea detector. Dynamic features are extracted from time-evolving spectral representation of photoplethysmography envelope recordings. In this regard, a time-evolving version of the standard linear multivariate decomposition is discussed to perform stochastic dimensionality reduction. For training aim, this work analyzes the concrete set comprising filter banked dynamic features that include spectral centroids, the cepstral coefficients as well as their time-variant energies. Performance of classifier accuracy is provided for the collected polysomnography recordings of 21 children. Moreover, since the apnea diagnosing is based on analysis of set of fragments partitioned from the photoplethysmography envelope recordings, a new approach for their indirect labeling is described. As a result, performed outcomes of accuracy bring enough evidence that if using a subset of cepstral-based dynamic features, then patient classification accuracy can reach as much as 83.3% value, when using a k-nn classifier, as well. Therefore, photoplethysmography-based detection provides an adequate scheme for obstructive sleep apnea diagnosis.
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title_short	Selection of Nonstationary Dynamic Features for Obstructive Sleep Apnoea Detection in Children
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Selection of Nonstationary Dynamic Features for Obstructive Sleep Apnoea Detection in Children

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