Dataset Information

Wavelet analysis of oximetry recordings to assist in the automated detection of moderate-to-severe pediatric sleep apnea-hypopnea syndrome.

ABSTRACT:

Background

The gold standard for pediatric sleep apnea hypopnea syndrome (SAHS) is overnight polysomnography, which has several limitations. Thus, simplified diagnosis techniques become necessary.

Objective

The 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.

Methods

981 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).

Results

The 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.

Conclusion

Wavelet 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.

SUBMITTER: Vaquerizo-Villar F

PROVIDER: S-EPMC6286069 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Wavelet analysis of oximetry recordings to assist in the automated detection of moderate-to-severe pediatric sleep apnea-hypopnea syndrome.

Vaquerizo-Villar Fernando F Álvarez Daniel D Kheirandish-Gozal Leila L Gutiérrez-Tobal Gonzalo C GC Barroso-García Verónica V Crespo Andrea A Del Campo Félix F Gozal David D Hornero Roberto R

PloS one 20181207 12

<h4>Background</h4>The gold standard for pediatric sleep apnea hypopnea syndrome (SAHS) is overnight polysomnography, which has several limitations. Thus, simplified diagnosis techniques become necessary.<h4>Objective</h4>The 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 ...[more]

PMID: 30532267

Similar Datasets

Project description:The coexistence of obstructive sleep apnea syndrome (OSAS) and chronic obstructive pulmonary disease (COPD) leads to increased morbidity and mortality. The development of home-based screening tests is essential to expedite diagnosis. Nevertheless, there is still very limited evidence on the effectiveness of portable monitoring to diagnose OSAS in patients with pulmonary comorbidities.To assess the influence of suffering from COPD in the performance of an oximetry-based screening test for moderate-to-severe OSAS, both in the hospital and at home.A total of 407 patients showing moderate-to-high clinical suspicion of OSAS were involved in the study. All subjects underwent (i) supervised portable oximetry simultaneously to in-hospital polysomnography (PSG) and (ii) unsupervised portable oximetry at home. A regression-based multilayer perceptron (MLP) artificial neural network (ANN) was trained to estimate the apnea-hypopnea index (AHI) from portable oximetry recordings. Two independent validation datasets were analyzed: COPD versus non-COPD.The portable oximetry-based MLP ANN reached similar intra-class correlation coefficient (ICC) values between the estimated AHI and the actual AHI for the non-COPD and the COPD groups either in the hospital (non-COPD: 0.937, 0.909-0.956 CI95%; COPD: 0.936, 0.899-0.960 CI95%) and at home (non-COPD: 0.731, 0.631-0.808 CI95%; COPD: 0.788, 0.678-0.864 CI95%). Regarding the area under the receiver operating characteristics curve (AUC), no statistically significant differences (p >0.01) between COPD and non-COPD groups were found in both settings, particularly for severe OSAS (AHI ?30 events/h): 0.97 (0.92-0.99 CI95%) non-COPD vs. 0.98 (0.92-1.0 CI95%) COPD in the hospital, and 0.87 (0.79-0.92 CI95%) non-COPD vs. 0.86 (0.75-0.93 CI95%) COPD at home.The agreement and the diagnostic performance of the estimated AHI from automated analysis of portable oximetry were similar regardless of the presence of COPD both in-lab and at-home. Particularly, portable oximetry could be used as an abbreviated screening test for moderate-to-severe OSAS in patients with COPD.

Project description:ObjectivesThe aim of this study was to identify correlations between sleep apnea severity and tongue volume or posterior airway space measured via three-dimensional reconstruction of volumetric computerized tomography (CT) images in patients with obstructive sleep apnea (OSA) for use in predicting OSA severity and in surgical treatment. We also assessed associations between tongue volume and Mallampati score.MethodsSnoring/OSA male patients (n = 64) who underwent polysomnography, cephalometry, and CT scans were enrolled in this retrospective study. OSA was diagnosed when the apnea-hypopnea index (AHI) was greater than 5 (mild 5-14; moderate 15-29; severe>30). The patients were also categorized into the normal-mild group (n = 22) and the moderate-severe group (n = 42). Using volumetric CT images with the three-dimensional reconstruction technique, the volume of the tongue, posterior airway space volume, and intra-mandibular space were measured. The volumes, polysomnographic parameters, and physical examination findings were compared, and independent factors that are related to OSA were analysed.ResultsNo associations between tongue volume or posterior airway space and the AHI were observed. However, multivariate linear analyses showed that tongue volume had significantly negative association with lowest O2 saturation (r = 0.365, p = 0.027). High BMI was related to an increase in tongue volume. Modified Mallampati scores showed borderline significant positive correlations with absolute tongue volume (r = 0.251, p = 0.046) and standardized tongue volume (absolute tongue volume / intramandibular area; r = 0.266, p = 0.034). Between the normal-mild and moderate-severe groups, absolute tongue volumes were not different, although the standardized tongue volume in the moderate-severe group was significantly higher.ConclusionAbsolute tongue volume showed stronger associations with lowest O2 saturation during sleep than with the severity of AHI. We also found that high BMI was a relevant factor for an increase in absolute tongue volume and modified Mallampati grading was a useful physical examination to predict tongue size.

Dataset Information

Wavelet analysis of oximetry recordings to assist in the automated detection of moderate-to-severe pediatric sleep apnea-hypopnea syndrome.

Background

Objective

Methods

Results

Conclusion

Publications

Wavelet analysis of oximetry recordings to assist in the automated detection of moderate-to-severe pediatric sleep apnea-hypopnea syndrome.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets