Abstract
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.
Highlights
The American Academy of Pediatrics (AAP) defines pediatric sleep apnea-hypopnea syndrome (SAHS) as a breathing disorder characterized by recurrent episodes of complete cessation and/or significant reduction of airflow during sleep [1]
Wavelet analysis of oximetry recordings to assist in moderate-to-severe pediatric sleep apnea detection diagnosis of pediatric sleep apnea hypopnea syndrome (SAHS) is difficult, and substantially more difficult than in adults, because the frequency of apneic events and reductions in SpO2 is markedly lower in children
We Wavelet analysis of oximetry recordings to assist in moderate-to-severe pediatric sleep apnea detection have assessed the diagnostic ability of our proposal using an apnea-hypopnea index (AHI) cutoff of 5 e/h, a widely used criterion in the clinical decision making leading to the recommendation of surgical treatment [3,10]
Summary
The American Academy of Pediatrics (AAP) defines pediatric sleep apnea-hypopnea syndrome (SAHS) as a breathing disorder characterized by recurrent episodes of complete cessation (apnea) and/or significant reduction (hypopnea) of airflow during sleep [1]. The gold standard technique for pediatric SAHS diagnosis is overnight polysomnography (PSG) It involves recording a wide range of biomedical signals in a specialized sleep laboratory [2,3]. To expedite the diagnosis and treatment is essential in these patients In this sense, surgical treatment with adenotonsillectomy is consistently recommended for children suffering from SAHS with an AHI 5 e/h [6]. In spite of the PSG serving as the current recommended diagnostic gold standard, it is costly and complex due to the necessary equipment and trained staff, as well as highly intrusive due to the use of multiple sensors It is a time-demanding method that shows limited availability and absent scalability, thereby delaying the diagnosis and treatment of SAHS patients [7,8].
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