Diagnosis of pediatric obstructive sleep apnea: Preliminary findings using automatic analysis of airflow and oximetry recordings obtained at patients’ home

Gutiérrez‐Tobal, Gonzalo C.; Alonso-Álvarez, M. Luz; Álvarez, Daniel; Campo, Félix del; Terán-Santos, Joaquín; Hornero, Roberto

doi:10.1016/j.bspc.2015.02.014

Cited by 35 publications

(78 citation statements)

References 29 publications

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“…As expected, spectral and nonlinear features, as well as ODI3, exhibited statistically significant differences between OSA-positive and OSAnegative children, which agrees with previous studies. 21,26 Then, automatic feature selection was accomplished by means of GAs in order to exhaustively analyze the feature space and obtain the optimum feature subset for each cutoff. We found that a proper selection of variables provided complementary information that improves the diagnostic ability of oximetry.…”

Section: Discussionmentioning

confidence: 99%

“…Recent studies have shown the usefulness of these automated methods in the context of childhood OSA diagnosis from supervised inlaboratory pulse oximetry and airflow signals. 21,26 In the current study, a comprehensive analysis of input parameters and frequency bands was carried out to properly characterize the influence of OSA in SpO 2 unattended recordings at home.…”

Section: Feature Extractionmentioning

confidence: 99%

“…The power spectral density was computed using the wellknown Welch method, which is suitable for nonstationary signals. 26,31 Next, a comprehensive analysis was conducted to properly derive a frequency region in the power spectrum related to the recurrence of desaturations in children. In order to obtain such a frequency band of interest, we searched for statistical significant differences in the power spectral density amplitude between OSA-positive and OSA-negative children for every single frequency component.…”

Section: Feature Extractionmentioning

confidence: 99%

“…In order to obtain such a frequency band of interest, we searched for statistical significant differences in the power spectral density amplitude between OSA-positive and OSA-negative children for every single frequency component. 26 Mean (M1fb), variance (M2fb), skewness (M3fb), and kurtosis (M4fb) were applied to quantify central tendency, dispersion, asymmetry, and peakedness of the power spectrum in the band of interest, respectively. In addition, other conventional spectral measures were also derived from this frequency band: maximum (PAb) and minimum (MAb) amplitudes and the signal relative power (RPb).…”

Section: Feature Extractionmentioning

confidence: 99%

“…In addition, other conventional spectral measures were also derived from this frequency band: maximum (PAb) and minimum (MAb) amplitudes and the signal relative power (RPb). 26 Finally, conventional oximetric indices commonly used in clinical practice were also included in the study. The ODI3, the minimum (minSat) and average (AvgSat) saturation, and the cumulative time spent below 90% (CT90) and 95% (CT95) saturation were computed.…”

Section: Feature Extractionmentioning

confidence: 99%

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Automated Screening of Children With Obstructive Sleep Apnea Using Nocturnal Oximetry: An Alternative to Respiratory Polygraphy in Unattended Settings

Álvarez

Alonso-Álvarez

Gutiérrez‐Tobal

et al. 2017

Journal of Clinical Sleep Medicine

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Study Objectives: Nocturnal oximetry has become known as a simple, readily available, and potentially useful diagnostic tool of childhood obstructive sleep apnea (OSA). However, at-home respiratory polygraphy (HRP) remains the preferred alternative to polysomnography (PSG) in unattended settings. The aim of this study was twofold: (1) to design and assess a novel methodology for pediatric OSA screening based on automated analysis of at-home oxyhemoglobin saturation (SpO2), and (2) to compare its diagnostic performance with HRP. Methods: SpO2 recordings were parameterized by means of time, frequency, and conventional oximetric measures. Logistic regression models were optimized using genetic algorithms (GAs) for three cutoffs for OSA: 1, 3, and 5 events/h. The diagnostic performance of logistic regression models, manual obstructive apnea-hypopnea index (OAHI) from HRP, and the conventional oxygen desaturation index ≥ 3% (ODI3) were assessed. Results: For a cutoff of 1 event/h, the optimal logistic regression model significantly outperformed both conventional HRP-derived ODI3 and OAHI: 85.5% accuracy (HRP 74.6%; ODI3 65.9%) and 0.97 area under the receiver operating characteristics curve (AUC) (HRP 0.78; ODI3 0.75) were reached. For a cutoff of 3 events/h, the logistic regression model achieved 83.4% accuracy (HRP 85.0%; ODI3 74.5%) and 0.96 AUC (HRP 0.93; ODI3 0.85) whereas using a cutoff of 5 events/h, oximetry reached 82.8% accuracy (HRP 85.1%; ODI3 76.7) and 0.97 AUC (HRP 0.95; ODI3 0.84). Conclusions: Automated analysis of at-home SpO2 recordings provide accurate detection of children with high pretest probability of OSA. Thus, unsupervised nocturnal oximetry may enable a simple and effective alternative to HRP and PSG in unattended settings. Keywords: at-home respiratory polygraphy, automated pattern recognition, blood oxygen saturation, genetic algorithms, nocturnal oximetry Citation: Álvarez D, Alonso-Álvarez ML, Gutiérrez-Tobal GC, Crespo A, Kheirandish-Gozal L, Hornero R, Gozal D, Terán-Santos J, Del Campo F. Automated screening of children with obstructive sleep apnea using nocturnal oximetry: an alternative to respiratory polygraphy in unattended settings. J Clin Sleep Med. 2017;13(5):693-702.

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Section: Discussionmentioning

confidence: 99%

Section: Feature Extractionmentioning

confidence: 99%

Section: Feature Extractionmentioning

confidence: 99%

Section: Feature Extractionmentioning

confidence: 99%

Section: Feature Extractionmentioning

confidence: 99%

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Automated Screening of Children With Obstructive Sleep Apnea Using Nocturnal Oximetry: An Alternative to Respiratory Polygraphy in Unattended Settings

Álvarez

Alonso-Álvarez

Gutiérrez‐Tobal

et al. 2017

Journal of Clinical Sleep Medicine

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Reliability of machine learning to diagnose pediatric obstructive sleep apnea: Systematic review and meta‐analysis

Gutiérrez‐Tobal

Álvarez

Kheirandish‐Gozal

et al. 2021

Pediatric Pulmonology

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Background Machine‐learning approaches have enabled promising results in efforts to simplify the diagnosis of pediatric obstructive sleep apnea (OSA). A comprehensive review and analysis of such studies increase the confidence level of practitioners and healthcare providers in the implementation of these methodologies in clinical practice. Objective To assess the reliability of machine‐learning‐based methods to detect pediatric OSA. Data Sources Two researchers conducted an electronic search on the Web of Science and Scopus using term, and studies were reviewed along with their bibliographic references. Eligibility Criteria Articles or reviews (Year 2000 onwards) that applied machine learning to detect pediatric OSA; reported data included information enabling derivation of true positive, false negative, true negative, and false positive cases; polysomnography served as diagnostic standard. Appraisal and Synthesis Methods Pooled sensitivities and specificities were computed for three apnea‐hypopnea index (AHI) thresholds: 1 event/hour (e/h), 5 e/h, and 10 e/h. Random‐effect models were assumed. Summary receiver‐operating characteristics (SROC) analyses were also conducted. Heterogeneity (I 2) was evaluated, and publication bias was corrected (trim and fill). Results Nineteen studies were finally retained, involving 4767 different pediatric sleep studies. Machine learning improved diagnostic performance as OSA severity criteria increased reaching optimal values for AHI = 10 e/h (0.652 sensitivity; 0.931 specificity; and 0.940 area under the SROC curve). Publication bias correction had minor effect on summary statistics, but high heterogeneity was observed among the studies.

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Network Analysis on Overnight EEG Spectrum to Assess Relationships Between Paediatric Sleep Apnoea and Cognition

et al. 2019

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Diagnosis of pediatric obstructive sleep apnea: Preliminary findings using automatic analysis of airflow and oximetry recordings obtained at patients’ home

Cited by 35 publications

References 29 publications

Automated Screening of Children With Obstructive Sleep Apnea Using Nocturnal Oximetry: An Alternative to Respiratory Polygraphy in Unattended Settings

Automated Screening of Children With Obstructive Sleep Apnea Using Nocturnal Oximetry: An Alternative to Respiratory Polygraphy in Unattended Settings

Reliability of machine learning to diagnose pediatric obstructive sleep apnea: Systematic review and meta‐analysis

Network Analysis on Overnight EEG Spectrum to Assess Relationships Between Paediatric Sleep Apnoea and Cognition

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