Cargando…
Wavelet analysis of oximetry recordings to assist in the automated detection of moderate-to-severe pediatric sleep apnea-hypopnea syndrome
BACKGROUND: The gold standard for pediatric sleep apnea hypopnea syndrome (SAHS) is overnight polysomnography, which has several limitations. Thus, simplified diagnosis techniques become necessary. OBJECTIVE: The aim of this study is twofold: (i) to analyze the blood oxygen saturation (SpO(2)) signa...
Autores principales: | , , , , , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2018
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6286069/ https://www.ncbi.nlm.nih.gov/pubmed/30532267 http://dx.doi.org/10.1371/journal.pone.0208502 |
_version_ | 1783379404093652992 |
---|---|
author | Vaquerizo-Villar, Fernando Álvarez, Daniel Kheirandish-Gozal, Leila Gutiérrez-Tobal, Gonzalo C. Barroso-García, Verónica Crespo, Andrea del Campo, Félix Gozal, David Hornero, Roberto |
author_facet | Vaquerizo-Villar, Fernando Álvarez, Daniel Kheirandish-Gozal, Leila Gutiérrez-Tobal, Gonzalo C. Barroso-García, Verónica Crespo, Andrea del Campo, Félix Gozal, David Hornero, Roberto |
author_sort | Vaquerizo-Villar, Fernando |
collection | PubMed |
description | BACKGROUND: The gold standard for pediatric sleep apnea hypopnea syndrome (SAHS) is overnight polysomnography, which has several limitations. Thus, simplified diagnosis techniques become necessary. OBJECTIVE: The aim of this study is twofold: (i) to analyze the blood oxygen saturation (SpO(2)) signal from nocturnal oximetry by means of features from the wavelet transform in order to characterize pediatric SAHS; (ii) to evaluate the usefulness of the extracted features to assist in the detection of pediatric SAHS. METHODS: 981 SpO(2) signals from children ranging 2–13 years of age were used. Discrete wavelet transform (DWT) was employed due to its suitability to deal with non-stationary signals as well as the ability to analyze the SAHS-related low frequency components of the SpO(2) signal with high resolution. In addition, 3% oxygen desaturation index (ODI3), statistical moments and power spectral density (PSD) features were computed. Fast correlation-based filter was applied to select a feature subset. This subset fed three classifiers (logistic regression, support vector machines (SVM), and multilayer perceptron) trained to determine the presence of moderate-to-severe pediatric SAHS (apnea-hypopnea index cutoff ≥ 5 events per hour). RESULTS: The wavelet entropy and features computed in the D(9) detail level of the DWT reached significant differences associated with the presence of SAHS. All the proposed classifiers fed with a selected feature subset composed of ODI3, statistical moments, PSD, and DWT features outperformed every single feature. SVM reached the highest performance. It achieved 84.0% accuracy (71.9% sensitivity, 91.1% specificity), outperforming state-of-the-art studies in the detection of moderate-to-severe SAHS using the SpO(2) signal alone. CONCLUSION: Wavelet analysis could be a reliable tool to analyze the oximetry signal in order to assist in the automated detection of moderate-to-severe pediatric SAHS. Hence, pediatric subjects suffering from moderate-to-severe SAHS could benefit from an accurate simplified screening test only using the SpO(2) signal. |
format | Online Article Text |
id | pubmed-6286069 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-62860692018-12-28 Wavelet analysis of oximetry recordings to assist in the automated detection of moderate-to-severe pediatric sleep apnea-hypopnea syndrome Vaquerizo-Villar, Fernando Álvarez, Daniel Kheirandish-Gozal, Leila Gutiérrez-Tobal, Gonzalo C. Barroso-García, Verónica Crespo, Andrea del Campo, Félix Gozal, David Hornero, Roberto PLoS One Research Article BACKGROUND: The gold standard for pediatric sleep apnea hypopnea syndrome (SAHS) is overnight polysomnography, which has several limitations. Thus, simplified diagnosis techniques become necessary. OBJECTIVE: The aim of this study is twofold: (i) to analyze the blood oxygen saturation (SpO(2)) signal from nocturnal oximetry by means of features from the wavelet transform in order to characterize pediatric SAHS; (ii) to evaluate the usefulness of the extracted features to assist in the detection of pediatric SAHS. METHODS: 981 SpO(2) signals from children ranging 2–13 years of age were used. Discrete wavelet transform (DWT) was employed due to its suitability to deal with non-stationary signals as well as the ability to analyze the SAHS-related low frequency components of the SpO(2) signal with high resolution. In addition, 3% oxygen desaturation index (ODI3), statistical moments and power spectral density (PSD) features were computed. Fast correlation-based filter was applied to select a feature subset. This subset fed three classifiers (logistic regression, support vector machines (SVM), and multilayer perceptron) trained to determine the presence of moderate-to-severe pediatric SAHS (apnea-hypopnea index cutoff ≥ 5 events per hour). RESULTS: The wavelet entropy and features computed in the D(9) detail level of the DWT reached significant differences associated with the presence of SAHS. All the proposed classifiers fed with a selected feature subset composed of ODI3, statistical moments, PSD, and DWT features outperformed every single feature. SVM reached the highest performance. It achieved 84.0% accuracy (71.9% sensitivity, 91.1% specificity), outperforming state-of-the-art studies in the detection of moderate-to-severe SAHS using the SpO(2) signal alone. CONCLUSION: Wavelet analysis could be a reliable tool to analyze the oximetry signal in order to assist in the automated detection of moderate-to-severe pediatric SAHS. Hence, pediatric subjects suffering from moderate-to-severe SAHS could benefit from an accurate simplified screening test only using the SpO(2) signal. Public Library of Science 2018-12-07 /pmc/articles/PMC6286069/ /pubmed/30532267 http://dx.doi.org/10.1371/journal.pone.0208502 Text en © 2018 Vaquerizo-Villar 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 Vaquerizo-Villar, Fernando Álvarez, Daniel Kheirandish-Gozal, Leila Gutiérrez-Tobal, Gonzalo C. Barroso-García, Verónica Crespo, Andrea del Campo, Félix Gozal, David Hornero, Roberto Wavelet analysis of oximetry recordings to assist in the automated detection of moderate-to-severe pediatric sleep apnea-hypopnea syndrome |
title | Wavelet analysis of oximetry recordings to assist in the automated detection of moderate-to-severe pediatric sleep apnea-hypopnea syndrome |
title_full | Wavelet analysis of oximetry recordings to assist in the automated detection of moderate-to-severe pediatric sleep apnea-hypopnea syndrome |
title_fullStr | Wavelet analysis of oximetry recordings to assist in the automated detection of moderate-to-severe pediatric sleep apnea-hypopnea syndrome |
title_full_unstemmed | Wavelet analysis of oximetry recordings to assist in the automated detection of moderate-to-severe pediatric sleep apnea-hypopnea syndrome |
title_short | Wavelet analysis of oximetry recordings to assist in the automated detection of moderate-to-severe pediatric sleep apnea-hypopnea syndrome |
title_sort | wavelet analysis of oximetry recordings to assist in the automated detection of moderate-to-severe pediatric sleep apnea-hypopnea syndrome |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6286069/ https://www.ncbi.nlm.nih.gov/pubmed/30532267 http://dx.doi.org/10.1371/journal.pone.0208502 |
work_keys_str_mv | AT vaquerizovillarfernando waveletanalysisofoximetryrecordingstoassistintheautomateddetectionofmoderatetoseverepediatricsleepapneahypopneasyndrome AT alvarezdaniel waveletanalysisofoximetryrecordingstoassistintheautomateddetectionofmoderatetoseverepediatricsleepapneahypopneasyndrome AT kheirandishgozalleila waveletanalysisofoximetryrecordingstoassistintheautomateddetectionofmoderatetoseverepediatricsleepapneahypopneasyndrome AT gutierreztobalgonzaloc waveletanalysisofoximetryrecordingstoassistintheautomateddetectionofmoderatetoseverepediatricsleepapneahypopneasyndrome AT barrosogarciaveronica waveletanalysisofoximetryrecordingstoassistintheautomateddetectionofmoderatetoseverepediatricsleepapneahypopneasyndrome AT crespoandrea waveletanalysisofoximetryrecordingstoassistintheautomateddetectionofmoderatetoseverepediatricsleepapneahypopneasyndrome AT delcampofelix waveletanalysisofoximetryrecordingstoassistintheautomateddetectionofmoderatetoseverepediatricsleepapneahypopneasyndrome AT gozaldavid waveletanalysisofoximetryrecordingstoassistintheautomateddetectionofmoderatetoseverepediatricsleepapneahypopneasyndrome AT horneroroberto waveletanalysisofoximetryrecordingstoassistintheautomateddetectionofmoderatetoseverepediatricsleepapneahypopneasyndrome |