Cargando…

Automatic unsupervised respiratory analysis of infant respiratory inductance plethysmography signals

Infants are at risk for potentially life-threatening postoperative apnea (POA). We developed an Automated Unsupervised Respiratory Event Analysis (AUREA) to classify breathing patterns obtained with dual belt respiratory inductance plethysmography and a reference using Expectation Maximization (EM)....

Descripción completa

Detalles Bibliográficos
Autores principales: Robles-Rubio, Carlos A., Kearney, Robert E., Bertolizio, Gianluca, Brown, Karen A.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7485851/
https://www.ncbi.nlm.nih.gov/pubmed/32915810
http://dx.doi.org/10.1371/journal.pone.0238402
_version_ 1783581230340505600
author Robles-Rubio, Carlos A.
Kearney, Robert E.
Bertolizio, Gianluca
Brown, Karen A.
author_facet Robles-Rubio, Carlos A.
Kearney, Robert E.
Bertolizio, Gianluca
Brown, Karen A.
author_sort Robles-Rubio, Carlos A.
collection PubMed
description Infants are at risk for potentially life-threatening postoperative apnea (POA). We developed an Automated Unsupervised Respiratory Event Analysis (AUREA) to classify breathing patterns obtained with dual belt respiratory inductance plethysmography and a reference using Expectation Maximization (EM). This work describes AUREA and evaluates its performance. AUREA computes six metrics and inputs them into a series of four binary k-means classifiers. Breathing patterns were characterized by normalized variance, nonperiodic power, instantaneous frequency and phase. Signals were classified sample by sample into one of 5 patterns: pause (PAU), movement (MVT), synchronous (SYB) and asynchronous (ASB) breathing, and unknown (UNK). MVT and UNK were combined as UNKNOWN. Twenty-one preprocessed records obtained from infants at risk for POA were analyzed. Performance was evaluated with a confusion matrix, overall accuracy, and pattern specific precision, recall, and F-score. Segments of identical patterns were evaluated for fragmentation and pattern matching with the EM reference. PAU exhibited very low normalized variance. MVT had high normalized nonperiodic power and low frequency. SYB and ASB had a median frequency of respectively, 0.76Hz and 0.71Hz, and a mode for phase of 4(o) and 100(o). Overall accuracy was 0.80. AUREA confused patterns most often with UNKNOWN (25.5%). The pattern specific F-score was highest for SYB (0.88) and lowest for PAU (0.60). PAU had high precision (0.78) and low recall (0.49). Fragmentation was evident in pattern events <2s. In 75% of the EM pattern events >2s, 50% of the samples classified by AUREA had identical patterns. Frequency and phase for SYB and ASB were consistent with published values for synchronous and asynchronous breathing in infants. The low normalized variance in PAU, was consistent with published scoring rules for pediatric apnea. These findings support the use of AUREA to classify breathing patterns and warrant a future evaluation of clinically relevant respiratory events.
format Online
Article
Text
id pubmed-7485851
institution National Center for Biotechnology Information
language English
publishDate 2020
publisher Public Library of Science
record_format MEDLINE/PubMed
spelling pubmed-74858512020-09-21 Automatic unsupervised respiratory analysis of infant respiratory inductance plethysmography signals Robles-Rubio, Carlos A. Kearney, Robert E. Bertolizio, Gianluca Brown, Karen A. PLoS One Research Article Infants are at risk for potentially life-threatening postoperative apnea (POA). We developed an Automated Unsupervised Respiratory Event Analysis (AUREA) to classify breathing patterns obtained with dual belt respiratory inductance plethysmography and a reference using Expectation Maximization (EM). This work describes AUREA and evaluates its performance. AUREA computes six metrics and inputs them into a series of four binary k-means classifiers. Breathing patterns were characterized by normalized variance, nonperiodic power, instantaneous frequency and phase. Signals were classified sample by sample into one of 5 patterns: pause (PAU), movement (MVT), synchronous (SYB) and asynchronous (ASB) breathing, and unknown (UNK). MVT and UNK were combined as UNKNOWN. Twenty-one preprocessed records obtained from infants at risk for POA were analyzed. Performance was evaluated with a confusion matrix, overall accuracy, and pattern specific precision, recall, and F-score. Segments of identical patterns were evaluated for fragmentation and pattern matching with the EM reference. PAU exhibited very low normalized variance. MVT had high normalized nonperiodic power and low frequency. SYB and ASB had a median frequency of respectively, 0.76Hz and 0.71Hz, and a mode for phase of 4(o) and 100(o). Overall accuracy was 0.80. AUREA confused patterns most often with UNKNOWN (25.5%). The pattern specific F-score was highest for SYB (0.88) and lowest for PAU (0.60). PAU had high precision (0.78) and low recall (0.49). Fragmentation was evident in pattern events <2s. In 75% of the EM pattern events >2s, 50% of the samples classified by AUREA had identical patterns. Frequency and phase for SYB and ASB were consistent with published values for synchronous and asynchronous breathing in infants. The low normalized variance in PAU, was consistent with published scoring rules for pediatric apnea. These findings support the use of AUREA to classify breathing patterns and warrant a future evaluation of clinically relevant respiratory events. Public Library of Science 2020-09-11 /pmc/articles/PMC7485851/ /pubmed/32915810 http://dx.doi.org/10.1371/journal.pone.0238402 Text en © 2020 Robles-Rubio et al http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Robles-Rubio, Carlos A.
Kearney, Robert E.
Bertolizio, Gianluca
Brown, Karen A.
Automatic unsupervised respiratory analysis of infant respiratory inductance plethysmography signals
title Automatic unsupervised respiratory analysis of infant respiratory inductance plethysmography signals
title_full Automatic unsupervised respiratory analysis of infant respiratory inductance plethysmography signals
title_fullStr Automatic unsupervised respiratory analysis of infant respiratory inductance plethysmography signals
title_full_unstemmed Automatic unsupervised respiratory analysis of infant respiratory inductance plethysmography signals
title_short Automatic unsupervised respiratory analysis of infant respiratory inductance plethysmography signals
title_sort automatic unsupervised respiratory analysis of infant respiratory inductance plethysmography signals
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7485851/
https://www.ncbi.nlm.nih.gov/pubmed/32915810
http://dx.doi.org/10.1371/journal.pone.0238402
work_keys_str_mv AT roblesrubiocarlosa automaticunsupervisedrespiratoryanalysisofinfantrespiratoryinductanceplethysmographysignals
AT kearneyroberte automaticunsupervisedrespiratoryanalysisofinfantrespiratoryinductanceplethysmographysignals
AT bertoliziogianluca automaticunsupervisedrespiratoryanalysisofinfantrespiratoryinductanceplethysmographysignals
AT brownkarena automaticunsupervisedrespiratoryanalysisofinfantrespiratoryinductanceplethysmographysignals