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Usefulness of Spectral Analysis of Respiratory Rate Variability to Help in Pediatric Sleep Apnea-Hypopnea Syndrome Diagnosis
41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society
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The sleep apnea-hypopnea syndrome (SAHS) is a chronic respiratory disorder of high prevalence among children (up to 4%). Nocturnal polysomnography (PSG) is the gold standard method to diagnose SAHS, which is a complex, expensive, and time-consuming test. Consequently, alternative simplified methods are demanded. We propose the analysis of the respiratory rate variability (RRV) signal, directly obtained from the airflow (AF) signals. The aim of our study is to evaluate the usefulness of the spectral information obtained from RRV in the diagnosis of pediatric SAHS. A database composed of 946 AF and blood oxygen saturation (SpO2) recordings from children between 0 and 13 years old was used. Our database was divided into four severity groups according to the apnea-hipopnea index (AHI): no-SAHS (AHI < 1 events/h), mild (1 events/h ≤ AHI < 5 events/h), moderate (5 events/h ≤ AHI < 10 events/h), and severe SAHS (AHI ≥ 10 events/h). RRV and 3% oxygen desaturation index (ODI3) were obtained from AF and SpO2 recordings, respectively. A spectral band of interest was determined (0.09–0.20 Hz.) and a total of 12 spectral features were extracted. Nine of these features showed statistically significant differences (p-value < 0.05) among the four severity groups. The spectral features from RRV along with ODI3 were used as inputs to binary logistic regression (LR) classifiers. The diagnostic performance of LR models were evaluated for the AHI cut-off points of 1, 5, and 10 e/h, achieving 66.5%, 84.0%, and 88.5% accuracy, respectively. These results outperformed those obtained by single ODI3. The joint use of the spectral information from RRV and ODI3 achieved a high diagnostic capability in the most severely-affected children, thus showing their complementarity. These results suggest that the information contained in RRV spectrum together with ODI3 is useful to help identify moderate-to-severe SAHS.
This work was supported by 'Ministerio de Ciencia, Innovación y Universidades' and ‘European Regional Development Fund (FEDER)’ under projects DPI2017-84280-R and RTC-2017-6516-1, and by ‘European Commission’ and ‘FEDER’ under project ‘POCTEP 0378_AD_EEGWA_2_P’. 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. 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). L. Kheirandish-Gozal and D. Gozal were supported by National Institutes of Health (NIH) grant HL130984. V. Barroso-García, G. C. Gutiérrez-Tobal, F. Vaquerizo-Villar, and R. Hornero, are with the Biomedical Engineering Group, Universidad de Valladolid, Spain (e-mail: firstname.lastname@example.org). D. Álvarez and F. del Campo are with the Hospital Universitario Río Hortega of Valladolid, Spain (e-mail: email@example.com). L. Kheirandish-Gozal and D. Gozal are with the Department of Child Health, The University of Missouri School of Medicine, Columbia, Missouri, USA (email: firstname.lastname@example.org).
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