Diagnosis of pediatric obstructive sleep apnea: Preliminary findings using automatic analysis of airflow and oximetry recordings obtained at patients' home. (April 2015)

• Record Type: Journal Article
• Authors: Gutiérrez-Tobal, Gonzalo C.; Alonso-Álvarez, M. Luz; Álvarez, Daniel; del Campo, Félix; Terán-Santos, Joaquín; Hornero, Roberto
• Is Part Of: Biomedical signal processing and control. Volume 18(2015)
• Page Start: 401
• Page End: 407
• Publication Date: 2015-04
• Subjects: Acc accuracy -- AF airflow -- AHI apnea-hypopnea index -- AROC area under the receiver-operating characteristics curve -- ECG electrocardiogram -- IQR interquartile range -- LR logistic regression -- MA maximum amplitude of the power spectral density -- mA minimum amplitude of the power spectral density -- Mf1-Mf4 first to fourth statistical moments of the power spectral density -- NPV negative predictive value -- ODI oxygen desaturation index -- OSAS obstructive sleep apnea syndrome -- PPV positive predictive value -- PSD power spectral density -- PSG polysomnography -- RP respiratory polygraphy -- Se sensitivity -- SLR stepwise logistic regression -- Sp specificity -- SpO2 oxygen saturation of the blood; Pediatric obstructive sleep apnea -- Airflow -- Oximetry -- Spectral analysis -- At-home assessment; Signal processing -- Periodicals; Biomedical engineering -- Periodicals; Signal Processing, Computer-Assisted -- Periodicals; Image Processing, Computer-Assisted -- Periodicals; Biomedical Engineering -- Periodicals; 610.28
• DOI: 10.1016/j.bspc.2015.02.014
• Languages: English
• ISSNs: 1746-8094

Abstract

Highlights: Spectral content of airflow in children was explored. Two respiratory bands related to pediatric obstructive sleep apnea were found. High diagnostic ability was shown when combining airflow and oximetry information. Our approach could be helpful in pediatric obstructive sleep apnea detection. Abstract: The obstructive sleep apnea syndrome (OSAS) greatly affects both the health and the quality of life of children. Therefore, an early diagnosis is crucial to avoid their severe consequences. However, the standard diagnostic test (polysomnography, PSG) is time-demanding, complex, and costly. We aim at assessing a new methodology for the pediatric OSAS diagnosis to reduce these drawbacks. Airflow (AF) and oxygen saturation (SpO2 ) at-home recordings from 50 children were automatically processed. Information from the spectrum of AF was evaluated, as well as combined with 3% oxygen desaturation index (ODI3) through a logistic regression model. A bootstrap methodology was conducted to validate the results. OSAS significantly increased the spectral content of AF at two abnormal frequency bands below (BW1) and above (BW2) the normal respiratory range. These novel bands are consistent with the occurrence of apneic events and the posterior respiratory overexertion, respectively. The spectral information from BW1 and BW2 showed complementarity both between them and with ODI3. A logistic regression model built with 3 AF spectral features (2 from BW1 and 1 from BW2) and ODI3 achieved (mean and 95% confidence interval): 85.9% sensitivity [64.5–98.7]; 87.4% specificity [70.2–98.6]; 86.3% accuracy [74.9–95.4]; 0.947 area under the receiver-operating characteristics curve [0.826–1]; 88.4% positive predictive value [72.3–98.5]; and 85.8% negative predictive value [65.8–98.5]. The combination of the spectral information from two novel AF bands with the ODI3 from SpO2 is useful for the diagnosis of OSAS in children.