Electromyography-based respiratory onset detection in COPD patients on non-invasive mechanical ventilation

To optimize long-term nocturnal non-invasive ventilation in patients with chronic obstructive pulmonary disease, surface diaphragm electromyography (EMGdi) might be helpful to detect patient-ventilator asynchrony. However, visual analysis is labor-intensive and EMGdi is heavily corrupted by electroc...

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Autores: Sarlabous Uranga, Leonardo|||0000-0002-0495-8422, Estrada Petrocelli, Luis Carlos, Cerezo Hernández, Ana, Leest, Sietske V. D., Torres Cebrián, Abel|||0000-0003-2678-1303, Jané Campos, Raimon|||0000-0002-6541-8729, Duiverman, Marieke, Garde Martínez, Ainara
Tipo de recurso: artículo
Fecha de publicación:2019
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:2117/176753
Acceso en línea:https://hdl.handle.net/2117/176753
https://dx.doi.org/10.3390/e21030258
Access Level:acceso abierto
Palabra clave:Electromyography
Fixed sample entropy
Adaptive filtering
Root mean square
Diaphragm electromyography
Non-invasive mechanical ventilation
Chronic obstructive pulmonary disease
Electromiografia
Àrees temàtiques de la UPC::Enginyeria biomèdica
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oai_identifier_str oai:upcommons.upc.edu:2117/176753
network_acronym_str ES
network_name_str España
repository_id_str
spelling Electromyography-based respiratory onset detection in COPD patients on non-invasive mechanical ventilationSarlabous Uranga, Leonardo|||0000-0002-0495-8422Estrada Petrocelli, Luis CarlosCerezo Hernández, AnaLeest, Sietske V. D.Torres Cebrián, Abel|||0000-0003-2678-1303Jané Campos, Raimon|||0000-0002-6541-8729Duiverman, MariekeGarde Martínez, AinaraElectromyographyFixed sample entropyAdaptive filteringRoot mean squareDiaphragm electromyographyNon-invasive mechanical ventilationChronic obstructive pulmonary diseaseElectromiografiaÀrees temàtiques de la UPC::Enginyeria biomèdicaTo optimize long-term nocturnal non-invasive ventilation in patients with chronic obstructive pulmonary disease, surface diaphragm electromyography (EMGdi) might be helpful to detect patient-ventilator asynchrony. However, visual analysis is labor-intensive and EMGdi is heavily corrupted by electrocardiographic (ECG) activity. Therefore, we developed an automatic method to detect inspiratory onset from EMGdi envelope using fixed sample entropy (fSE) and a dynamic threshold based on kernel density estimation (KDE). Moreover, we combined fSE with adaptive filtering techniques to reduce ECG interference and improve onset detection. The performance of EMGdi envelopes extracted by applying fSE and fSE with adaptive filtering was compared to the root mean square (RMS)-based envelope provided by the EMG acquisition device. Automatic onset detection accuracy, using these three envelopes, was evaluated through the root mean square error (RMSE) between the automatic and mean visual onsets (made by two observers). The fSE-based method provided lower RMSE, which was reduced from 298 ms to 264 ms when combined with adaptive filtering, compared to 301 ms provided by the RMS-based method. The RMSE was negatively correlated with the proposed EMGdi quality indices. Following further validation, fSE with KDE, combined with adaptive filtering when dealing with low quality EMGdi, indicates promise for detecting the neural onset of respiratory drive.Peer Reviewed20192019-03-0720202020-02-04journal articlehttp://purl.org/coar/resource_type/c_6501VoRhttp://purl.org/coar/version/c_970fb48d4fbd8a85info:eu-repo/semantics/articleapplication/pdfhttps://hdl.handle.net/2117/176753https://dx.doi.org/10.3390/e21030258reponame:UPCommons. Portal del coneixement obert de la UPCinstname:Universitat Politècnica de Catalunya (UPC)Inglésengopen accesshttp://purl.org/coar/access_right/c_abf2Attribution 4.0 Internationalhttp://creativecommons.org/licenses/by-nc-nd/3.0/es/info:eu-repo/semantics/openAccessoai:upcommons.upc.edu:2117/1767532026-05-27T15:37:01Z
dc.title.none.fl_str_mv Electromyography-based respiratory onset detection in COPD patients on non-invasive mechanical ventilation
title Electromyography-based respiratory onset detection in COPD patients on non-invasive mechanical ventilation
spellingShingle Electromyography-based respiratory onset detection in COPD patients on non-invasive mechanical ventilation
Sarlabous Uranga, Leonardo|||0000-0002-0495-8422
Electromyography
Fixed sample entropy
Adaptive filtering
Root mean square
Diaphragm electromyography
Non-invasive mechanical ventilation
Chronic obstructive pulmonary disease
Electromiografia
Àrees temàtiques de la UPC::Enginyeria biomèdica
title_short Electromyography-based respiratory onset detection in COPD patients on non-invasive mechanical ventilation
title_full Electromyography-based respiratory onset detection in COPD patients on non-invasive mechanical ventilation
title_fullStr Electromyography-based respiratory onset detection in COPD patients on non-invasive mechanical ventilation
title_full_unstemmed Electromyography-based respiratory onset detection in COPD patients on non-invasive mechanical ventilation
title_sort Electromyography-based respiratory onset detection in COPD patients on non-invasive mechanical ventilation
dc.creator.none.fl_str_mv Sarlabous Uranga, Leonardo|||0000-0002-0495-8422
Estrada Petrocelli, Luis Carlos
Cerezo Hernández, Ana
Leest, Sietske V. D.
Torres Cebrián, Abel|||0000-0003-2678-1303
Jané Campos, Raimon|||0000-0002-6541-8729
Duiverman, Marieke
Garde Martínez, Ainara
author Sarlabous Uranga, Leonardo|||0000-0002-0495-8422
author_facet Sarlabous Uranga, Leonardo|||0000-0002-0495-8422
Estrada Petrocelli, Luis Carlos
Cerezo Hernández, Ana
Leest, Sietske V. D.
Torres Cebrián, Abel|||0000-0003-2678-1303
Jané Campos, Raimon|||0000-0002-6541-8729
Duiverman, Marieke
Garde Martínez, Ainara
author_role author
author2 Estrada Petrocelli, Luis Carlos
Cerezo Hernández, Ana
Leest, Sietske V. D.
Torres Cebrián, Abel|||0000-0003-2678-1303
Jané Campos, Raimon|||0000-0002-6541-8729
Duiverman, Marieke
Garde Martínez, Ainara
author2_role author
author
author
author
author
author
author
dc.subject.none.fl_str_mv Electromyography
Fixed sample entropy
Adaptive filtering
Root mean square
Diaphragm electromyography
Non-invasive mechanical ventilation
Chronic obstructive pulmonary disease
Electromiografia
Àrees temàtiques de la UPC::Enginyeria biomèdica
topic Electromyography
Fixed sample entropy
Adaptive filtering
Root mean square
Diaphragm electromyography
Non-invasive mechanical ventilation
Chronic obstructive pulmonary disease
Electromiografia
Àrees temàtiques de la UPC::Enginyeria biomèdica
description To optimize long-term nocturnal non-invasive ventilation in patients with chronic obstructive pulmonary disease, surface diaphragm electromyography (EMGdi) might be helpful to detect patient-ventilator asynchrony. However, visual analysis is labor-intensive and EMGdi is heavily corrupted by electrocardiographic (ECG) activity. Therefore, we developed an automatic method to detect inspiratory onset from EMGdi envelope using fixed sample entropy (fSE) and a dynamic threshold based on kernel density estimation (KDE). Moreover, we combined fSE with adaptive filtering techniques to reduce ECG interference and improve onset detection. The performance of EMGdi envelopes extracted by applying fSE and fSE with adaptive filtering was compared to the root mean square (RMS)-based envelope provided by the EMG acquisition device. Automatic onset detection accuracy, using these three envelopes, was evaluated through the root mean square error (RMSE) between the automatic and mean visual onsets (made by two observers). The fSE-based method provided lower RMSE, which was reduced from 298 ms to 264 ms when combined with adaptive filtering, compared to 301 ms provided by the RMS-based method. The RMSE was negatively correlated with the proposed EMGdi quality indices. Following further validation, fSE with KDE, combined with adaptive filtering when dealing with low quality EMGdi, indicates promise for detecting the neural onset of respiratory drive.
publishDate 2019
dc.date.none.fl_str_mv 2019
2019-03-07
2020
2020-02-04
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/2117/176753
https://dx.doi.org/10.3390/e21030258
url https://hdl.handle.net/2117/176753
https://dx.doi.org/10.3390/e21030258
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 4.0 International
http://creativecommons.org/licenses/by-nc-nd/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 4.0 International
http://creativecommons.org/licenses/by-nc-nd/3.0/es/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
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 15,301629

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