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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Detalhes bibliográficos
Autores: Sarlabous, Leonardo, Estrada, Luis, Cerezo Hernández, Ana, Leets, Sietske V. D., Torres, Abel, Jané, Raimon, Duiverman, Marieke, Garde, Ainara
Formato: artículo
Estado:Versión publicada
Fecha de publicación:2019
País:España
Recursos:Universidad de Barcelona
Repositorio:Dipòsit Digital de la UB
OAI Identifier:oai:diposit.ub.edu:2445/186184
Acesso em linha:https://hdl.handle.net/2445/186184
Access Level:acceso abierto
Palavra-chave:Respiració artificial
Malalties pulmonars obstructives cròniques
Artificial respiration
Chronic obstructive pulmonary diseases
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spelling Electromyography-based respiratory onset detection in copd patients on non-invasive mechanical ventilationSarlabous, LeonardoEstrada, LuisCerezo Hernández, AnaLeets, Sietske V. D.Torres, AbelJané, RaimonDuiverman, MariekeGarde, AinaraRespiració artificialMalalties pulmonars obstructives cròniquesArtificial respirationChronic obstructive pulmonary diseasesTo 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.2019info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfhttps://hdl.handle.net/2445/186184Articles publicats en revistes (Institut de Bioenginyeria de Catalunya (IBEC))reponame:Dipòsit Digital de la UBinstname:Universidad de BarcelonaInglésReproducció del document publicat a: https://doi.org/10.3390/e21030258Entropy, 2019, vol. 21, num. 3, p. 258https://doi.org/10.3390/e21030258cc by (c) Sarlabous, Leonardo et al, 2019http://creativecommons.org/licenses/by/3.0/es/info:eu-repo/semantics/openAccessoai:diposit.ub.edu:2445/1861842026-05-27T06:46:51Z
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, Leonardo
Respiració artificial
Malalties pulmonars obstructives cròniques
Artificial respiration
Chronic obstructive pulmonary diseases
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, Leonardo
Estrada, Luis
Cerezo Hernández, Ana
Leets, Sietske V. D.
Torres, Abel
Jané, Raimon
Duiverman, Marieke
Garde, Ainara
author Sarlabous, Leonardo
author_facet Sarlabous, Leonardo
Estrada, Luis
Cerezo Hernández, Ana
Leets, Sietske V. D.
Torres, Abel
Jané, Raimon
Duiverman, Marieke
Garde, Ainara
author_role author
author2 Estrada, Luis
Cerezo Hernández, Ana
Leets, Sietske V. D.
Torres, Abel
Jané, Raimon
Duiverman, Marieke
Garde, Ainara
author2_role author
author
author
author
author
author
author
dc.subject.none.fl_str_mv Respiració artificial
Malalties pulmonars obstructives cròniques
Artificial respiration
Chronic obstructive pulmonary diseases
topic Respiració artificial
Malalties pulmonars obstructives cròniques
Artificial respiration
Chronic obstructive pulmonary diseases
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
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv https://hdl.handle.net/2445/186184
url https://hdl.handle.net/2445/186184
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv Reproducció del document publicat a: https://doi.org/10.3390/e21030258
Entropy, 2019, vol. 21, num. 3, p. 258
https://doi.org/10.3390/e21030258
dc.rights.none.fl_str_mv cc by (c) Sarlabous, Leonardo et al, 2019
http://creativecommons.org/licenses/by/3.0/es/
info:eu-repo/semantics/openAccess
rights_invalid_str_mv cc by (c) Sarlabous, Leonardo et al, 2019
http://creativecommons.org/licenses/by/3.0/es/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.source.none.fl_str_mv Articles publicats en revistes (Institut de Bioenginyeria de Catalunya (IBEC))
reponame:Dipòsit Digital de la UB
instname:Universidad de Barcelona
instname_str Universidad de Barcelona
reponame_str Dipòsit Digital de la UB
collection Dipòsit Digital de la UB
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15,298079

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