Wavelet analysis of oximetry recordings to assist in the automated detection of moderate-to-severe pediatric sleep apnea-hypopnea syndrome
Producción Científica
| Autores: | , , , , , , , , |
|---|---|
| Tipo de recurso: | artículo |
| Estado: | Versión publicada |
| Fecha de publicación: | 2018 |
| País: | España |
| Institución: | Universidad de Valladolid |
| Repositorio: | UVaDOC. Repositorio Documental de la Universidad de Valladolid |
| OAI Identifier: | oai:uvadoc.uva.es:10324/80297 |
| Acceso en línea: | https://doi.org/10.1371/journal.pone.0208502 https://uvadoc.uva.es/handle/10324/80297 |
| Access Level: | acceso abierto |
| Palabra clave: | 1203.04 Inteligencia Artificial 3325 Tecnología de las Telecomunicaciones 3314 Tecnología Médica |
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| dc.title.none.fl_str_mv |
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 |
| spellingShingle |
Wavelet analysis of oximetry recordings to assist in the automated detection of moderate-to-severe pediatric sleep apnea-hypopnea syndrome Vaquerizo Villar, Fernando 1203.04 Inteligencia Artificial 3325 Tecnología de las Telecomunicaciones 3314 Tecnología Médica |
| title_short |
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_sort |
Wavelet analysis of oximetry recordings to assist in the automated detection of moderate-to-severe pediatric sleep apnea-hypopnea syndrome |
| dc.creator.none.fl_str_mv |
Vaquerizo Villar, Fernando Álvarez González, Daniel Kheirandish Gozal, Leila Gutierrez Tobal, Gonzalo César Barroso García, Verónica Crespo, Andrea Campo Matias, Félix del Gozal, David Hornero Sánchez, Roberto |
| author |
Vaquerizo Villar, Fernando |
| author_facet |
Vaquerizo Villar, Fernando Álvarez González, Daniel Kheirandish Gozal, Leila Gutierrez Tobal, Gonzalo César Barroso García, Verónica Crespo, Andrea Campo Matias, Félix del Gozal, David Hornero Sánchez, Roberto |
| author_role |
author |
| author2 |
Álvarez González, Daniel Kheirandish Gozal, Leila Gutierrez Tobal, Gonzalo César Barroso García, Verónica Crespo, Andrea Campo Matias, Félix del Gozal, David Hornero Sánchez, Roberto |
| author2_role |
author author author author author author author author |
| dc.subject.none.fl_str_mv |
1203.04 Inteligencia Artificial 3325 Tecnología de las Telecomunicaciones 3314 Tecnología Médica |
| topic |
1203.04 Inteligencia Artificial 3325 Tecnología de las Telecomunicaciones 3314 Tecnología Médica |
| description |
Producción Científica |
| publishDate |
2018 |
| dc.date.none.fl_str_mv |
2018 |
| dc.type.none.fl_str_mv |
info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion |
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article |
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publishedVersion |
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https://doi.org/10.1371/journal.pone.0208502 https://uvadoc.uva.es/handle/10324/80297 |
| url |
https://doi.org/10.1371/journal.pone.0208502 https://uvadoc.uva.es/handle/10324/80297 |
| dc.language.none.fl_str_mv |
Inglés |
| language_invalid_str_mv |
Inglés |
| dc.relation.none.fl_str_mv |
https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0208502 |
| dc.rights.none.fl_str_mv |
info:eu-repo/semantics/openAccess |
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openAccess |
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application/pdf |
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PLOS |
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PLOS |
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reponame:UVaDOC. Repositorio Documental de la Universidad de Valladolid instname:Universidad de Valladolid |
| instname_str |
Universidad de Valladolid |
| reponame_str |
UVaDOC. Repositorio Documental de la Universidad de Valladolid |
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UVaDOC. Repositorio Documental de la Universidad de Valladolid |
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| _version_ |
1869419435547164672 |
| spelling |
Wavelet analysis of oximetry recordings to assist in the automated detection of moderate-to-severe pediatric sleep apnea-hypopnea syndromeVaquerizo Villar, FernandoÁlvarez González, DanielKheirandish Gozal, LeilaGutierrez Tobal, Gonzalo CésarBarroso García, VerónicaCrespo, AndreaCampo Matias, Félix delGozal, DavidHornero Sánchez, Roberto1203.04 Inteligencia Artificial3325 Tecnología de las Telecomunicaciones3314 Tecnología MédicaProducción CientíficaBackground 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 (SpO2) 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 SpO2 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 SpO2 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 D9 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 SpO2 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 SpO2 signal.This work was supported by 'Agencia Estatal de Investigación del Ministerio de Ciencia, Innovación y Universidades' and ‘European Regional Development Fund (FEDER)’ under projects DPI2017-84280-R, RTC-2015-3446-1, and 0378_AD_EEGWA_2_P, by ‘Consejería de Educación de la Junta de Castilla y León and FEDER’ under project VA037U16, and by ‘European Commission’ and ‘FEDER’ under project ‘Análisis y correlación entre el genoma completo y la actividad cerebral para la ayuda en el diagnóstico de la enfermedad de Alzheimer’ (‘Cooperation Pro- gramme Interreg V-A Spain-Portugal POCTEP 2014–2020’). F. Vaquerizo-Villar was in receipt of a ‘Ayuda para contratos predoctorales para la Formación de Profesorado Universitario (FPU)’ grant from the Ministerio de Educación, Cultura y Deporte (FPU16/02938). V. Barroso-García was in a receipt of a ‘Ayuda para financiar la contratación predoctoral de personal investigador’ grant from the Consejería de Educación de la Junta de Castilla y León and the European Social Fund. D. Álvarez was in receipt of a Juan de la Cierva grant from MINECO (IJCI-2014-22664). L. Kheirandish-Gozal was supported by National Institutes of Health (NIH) grant HL130984 and D. Gozal by NIH grant HL-65270.PLOS2018info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfhttps://doi.org/10.1371/journal.pone.0208502https://uvadoc.uva.es/handle/10324/80297reponame:UVaDOC. Repositorio Documental de la Universidad de Valladolidinstname:Universidad de ValladolidIngléshttps://journals.plos.org/plosone/article?id=10.1371/journal.pone.0208502info:eu-repo/semantics/openAccessoai:uvadoc.uva.es:10324/802972026-06-13T12:44:47Z |
| score |
15,811543 |