RT info:eu-repo/semantics/article
T1 Wavelet analysis of oximetry recordings to assist in the automated detection of moderate-to-severe pediatric sleep apnea-hypopnea syndrome
A1 Vaquerizo-Villar, Fernando
A1 Álvarez, Daniel
A1 Kheirandish-Gozal, Leila
A1 Gutiérrez-Tobal, Gonzalo C.
A1 Barroso-García, Verónica
A1 Crespo, Andrea
A1 del Campo, Félix
A1 Gozal, David
A1 Hornero, Roberto
K1 1203.04 Inteligencia Artificial
K1 3325 Tecnología de las Telecomunicaciones
K1 3314 Tecnología Médica
AB BackgroundThe gold standard for pediatric sleep apnea hypopnea syndrome (SAHS) is overnight polysomnography, which has several limitations. Thus, simplified diagnosis techniques become necessary.ObjectiveThe 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.Methods981 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).ResultsThe 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.ConclusionWavelet 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.
PB PLOS
SN 1932-6203
YR 2018
FD 2018-12-07
LK https://uvadoc.uva.es/handle/10324/80297
UL https://uvadoc.uva.es/handle/10324/80297
LA eng
NO PLoS ONE, Diciembre 2025 vol. 13, n. 12, p. e0208502.
NO Producción Científica
DS UVaDOC
RD 11-ene-2026