Show simple item record

dc.contributor.authorEstrada Petrocelli, Luis
dc.contributor.authorTorres Cebrián, Abel
dc.contributor.authorSarlabous Uranga, Leonardo
dc.contributor.authorJané Campos, Raimon
dc.contributor.otherUniversitat Politècnica de Catalunya. Departament d'Enginyeria de Sistemes, Automàtica i Informàtica Industrial
dc.date.accessioned2018-03-16T13:32:19Z
dc.date.issued2018-01-01
dc.identifier.citationEstrada, L., Torres, A., Sarlabous, L., Jane, R. Onset and offset estimation of the neural inspiratory time in surface diaphragm electromyography: a pilot study in healthy subjects. "IEEE Journal of Biomedical and Health Informatics", 1 Gener 2018, vol. 22, núm. 1, p. 67.
dc.identifier.issn2168-2194
dc.identifier.urihttp://hdl.handle.net/2117/115292
dc.description.abstract© 2013 IEEE. This study evaluates the onset and offset of neural inspiratory time estimated from surface diaphragm electromyographic (EMGdi) recordings. EMGdi and airflow signals were recorded in ten healthy subjects according to two respiratory protocols based on respiratory rate (RR) increments, from 15 to 40 breaths per minute (bpm), and fractional inspiratory time (Ti/Ttot) decrements, from 0.54 to 0.18. The analysis of EMGdi signal amplitude is an alternative approach for the quantification of neural respiratory drive. The EMGdi amplitude was estimated using the fixed sample entropy computed over a 250 ms moving window of the EMGdi signal (EMGdifse). The neural onset was detected through a dynamic threshold over the EMGdifse using the kernel density estimation method, while neural offset was detected by finding when the EMGdifse had decreased to 70% of the peak value reached during inspiration. The Bland-Altman analysis between airflow and neural onsets showed a global bias of 46 ms in the RR protocol and 22 ms in the Ti/Ttot protocol. The Bland-Altman analysis between airflow and neural offsets reveals a global bias of 11 ms in the RR protocol and -2 ms in the Ti/T tot protocol. The relationship between pairs of RR values (Pearson's correlation coefficient of 0.99, Bland-=Altman limits of -2.39 to 2.41 bpm, and mean bias of 0.01 bpm) and between pairs of Ti/Ttot values (Pearson's correlation coefficient of 0.86, Bland-Altman limits of -0.11 to 0.10, and mean bias of -0.01) showed a good agreement. In conclusion, we propose a method for determining neural onset and neural offset based on noninvasive recordings of the electrical activity of the diaphragm that requires no filtering of cardiac muscle interference.
dc.format.extent1 p.
dc.language.isoeng
dc.rightsAttribution-NonCommercial-NoDerivs 3.0 Spain
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/es/
dc.subjectÀrees temàtiques de la UPC::Enginyeria biomèdica
dc.subject.lcshBiomedical engineering
dc.subject.otherFixed sample entropy (fSampEn)
dc.subject.otherinspiratory time
dc.subject.otherkernel density estimation (KDE)
dc.subject.otherneural inspiratory time
dc.subject.otherneural respiratory drive (NRD)
dc.subject.othersurface diaphragm electromyographic (EMGdi) signal
dc.titleOnset and offset estimation of the neural inspiratory time in surface diaphragm electromyography: a pilot study in healthy subjects
dc.typeArticle
dc.subject.lemacEnginyeria biomèdica
dc.contributor.groupUniversitat Politècnica de Catalunya. BIOSPIN - Biomedical Signal Processing and Interpretation
dc.identifier.doi10.1109/JBHI.2017.2672800
dc.description.peerreviewedPeer Reviewed
dc.relation.publisherversionhttp://ieeexplore.ieee.org/document/7862161/
dc.rights.accessRestricted access - publisher's policy
local.identifier.drac21870351
dc.description.versionPostprint (published version)
dc.date.lift10000-01-01
local.citation.authorEstrada, L.; Torres, A.; Sarlabous, L.; Jane, R.
local.citation.publicationNameIEEE Journal of Biomedical and Health Informatics
local.citation.volume22
local.citation.number1
local.citation.startingPage67
local.citation.endingPage67


Files in this item

Thumbnail

This item appears in the following Collection(s)

Show simple item record

Attribution-NonCommercial-NoDerivs 3.0 Spain
Except where otherwise noted, content on this work is licensed under a Creative Commons license : Attribution-NonCommercial-NoDerivs 3.0 Spain