Assessment of oximetry-based statistical classifiers as simplified screening tools in the management of childhood obstructive sleep apnea

Crespo, Andrea; Álvarez, Daniel; Kheirandish‐Gozal, Leila; Gutiérrez‐Tobal, Gonzalo C.; Cerezo-Hernández, Ana; Gozal, David; Hornero, Roberto; Campo, Félix del

doi:10.1007/s11325-018-1637-3

Cited by 27 publications

(68 citation statements)

References 28 publications

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“…Entropy is commonly used to characterize the dynamics of complex biological systems, so that the higher the entropy the higher the irregularity. In the framework of oximetry and OSAS, there is a close relation between entropy, ODI3, and the AHI [25,29,30]. It is important to point out that different metrics have been proposed to estimate entropy.…”

Section: Advanced Signal Processing and Automated Pattern Recognitionmentioning

confidence: 99%

Oximetry use in obstructive sleep apnea

Campo

Crespo

Cerezo-Hernández

et al. 2018

Expert Review of Respiratory Medicine

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Introduction. Overnight oximetry has been proposed as an accessible, simple, and reliable technique for obstructive sleep apnea syndrome (OSAS) diagnosis. From visual inspection to advanced signal processing, several studies have demonstrated the usefulness of oximetry as a screening tool. However, there is still controversy regarding the general application of oximetry as a single screening methodology for OSAS.Areas covered. Currently, high-resolution portable devices combined with pattern recognitionbased applications are able to achieve high performance in the detection this disease. In this review, recent studies involving automated analysis of oximetry by means of advanced signal processing and machine learning algorithms are analyzed. Advantages and limitations are highlighted and novel research lines aimed at improving the screening ability of oximetry are proposed. Expert commentary. Oximetry is a cost-effective tool for OSAS screening in patients showinghigh pretest probability for the disease. Nevertheless, exhaustive analyses are still needed to further assess unattended oximetry monitoring as a single diagnostic test for sleep apnea, particularly in the pediatric population and in especial groups with significant comorbidities. In the following years, communication technologies and big data analysis will overcome current limitations of simplified sleep testing approaches, changing the detection and management of OSAS.

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Section: Advanced Signal Processing and Automated Pattern Recognitionmentioning

confidence: 99%

Oximetry use in obstructive sleep apnea

Campo

Crespo

Cerezo-Hernández

et al. 2018

Expert Review of Respiratory Medicine

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“…One of the most promising due to its ease of use and low cost is the NOS, which mainly focuses on measuring the SpO2 signal, coupled with an ML- or DL-based classifier. Table 1 presents a summary of a number of these tools recently developed for symptomatic adults [ 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 ] and the pediatric population [ 28 , 29 , 30 , 31 , 32 , 33 , 34 ].…”

Section: Discussionmentioning

confidence: 99%

“…Even when performing similar calculations, the extracted features are different or are used differently. For example, several papers used time, frequency and nonlinear domain calculations, but few or even none of them match [ 22 , 29 , 30 , 31 , 34 ]. There is also disparity when performing spectral calculations.…”

Section: Discussionmentioning

confidence: 99%

Development of a Minimally Invasive Screening Tool to Identify Obese Pediatric Population at Risk of Obstructive Sleep Apnea/Hypopnea Syndrome

et al. 2020

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Obstructive sleep apnea syndrome is a reduction of the airflow during sleep which not only produces a reduction in sleep quality but also has major health consequences. The prevalence in the obese pediatric population can surpass 50%, and polysomnography is the current gold standard method for its diagnosis. Unfortunately, it is expensive, disturbing and time-consuming for experienced professionals. The objective is to develop a patient-friendly screening tool for the obese pediatric population to identify those children at higher risk of suffering from this syndrome. Three supervised learning classifier algorithms (i.e., logistic regression, support vector machine and AdaBoost) common in the field of machine learning were trained and tested on two very different datasets where oxygen saturation raw signal was recorded. The first dataset was the Childhood Adenotonsillectomy Trial (CHAT) consisting of 453 individuals, with ages between 5 and 9 years old and one-third of the patients being obese. Cross-validation was performed on the second dataset from an obesity assessment consult at the Pediatric Department of the Hospital General Universitario of Valencia. A total of 27 patients were recruited between 5 and 17 years old; 42% were girls and 63% were obese. The performance of each algorithm was evaluated based on key performance indicators (e.g., area under the curve, accuracy, recall, specificity and positive predicted value). The logistic regression algorithm outperformed (accuracy = 0.79, specificity = 0.96, area under the curve = 0.9, recall = 0.62 and positive predictive value = 0.94) the support vector machine and the AdaBoost algorithm when trained with the CHAT datasets. Cross-validation tests, using the Hospital General de Valencia (HG) dataset, confirmed the higher performance of the logistic regression algorithm in comparison with the others. In addition, only a minor loss of performance (accuracy = 0.75, specificity = 0.88, area under the curve = 0.85, recall = 0.62 and positive predictive value = 0.83) was observed despite the differences between the datasets. The proposed minimally invasive screening tool has shown promising performance when it comes to identifying children at risk of suffering obstructive sleep apnea syndrome. Moreover, it is ideal to be implemented in an outpatient consult in primary and secondary care.

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“…From a machine learning point of view, previous studies focused on the automatic detection of pediatric OSA using simple and widespread algorithms. These approaches included Logistic Regression (LR) [ 23 , 24 , 25 , 27 , 28 , 30 , 31 , 36 , 37 ], Linear or Quadratic Discriminant Analysis (LDA, QDA) [ 38 , 39 , 40 ], and Support Vector Machines (SVM) [ 41 ]. Other recent studies relied on more complex Multilayer Perceptron (MLP) neural networks [ 26 , 29 , 32 , 42 , 43 ].…”

Section: Introductionmentioning

confidence: 99%

Assessment of Airflow and Oximetry Signals to Detect Pediatric Sleep Apnea-Hypopnea Syndrome Using AdaBoost

Jiménez-García

Gutiérrez‐Tobal

García

et al. 2020

Entropy

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The reference standard to diagnose pediatric Obstructive Sleep Apnea (OSA) syndrome is an overnight polysomnographic evaluation. When polysomnography is either unavailable or has limited availability, OSA screening may comprise the automatic analysis of a minimum number of signals. The primary objective of this study was to evaluate the complementarity of airflow (AF) and oximetry (SpO2) signals to automatically detect pediatric OSA. Additionally, a secondary goal was to assess the utility of a multiclass AdaBoost classifier to predict OSA severity in children. We extracted the same features from AF and SpO2 signals from 974 pediatric subjects. We also obtained the 3% Oxygen Desaturation Index (ODI) as a common clinically used variable. Then, feature selection was conducted using the Fast Correlation-Based Filter method and AdaBoost classifiers were evaluated. Models combining ODI 3% and AF features outperformed the diagnostic performance of each signal alone, reaching 0.39 Cohens’s kappa in the four-class classification task. OSA vs. No OSA accuracies reached 81.28%, 82.05% and 90.26% in the apnea–hypopnea index cutoffs 1, 5 and 10 events/h, respectively. The most relevant information from SpO2 was redundant with ODI 3%, and AF was complementary to them. Thus, the joint analysis of AF and SpO2 enhanced the diagnostic performance of each signal alone using AdaBoost, thereby enabling a potential screening alternative for OSA in children.

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Assessment of oximetry-based statistical classifiers as simplified screening tools in the management of childhood obstructive sleep apnea

Abstract: Automated analysis of overnight oximetry may be used to develop reliable as well as accurate screening tools for childhood OSAS.

Cited by 27 publications

References 28 publications

Oximetry use in obstructive sleep apnea

Oximetry use in obstructive sleep apnea

Development of a Minimally Invasive Screening Tool to Identify Obese Pediatric Population at Risk of Obstructive Sleep Apnea/Hypopnea Syndrome

Assessment of Airflow and Oximetry Signals to Detect Pediatric Sleep Apnea-Hypopnea Syndrome Using AdaBoost

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