Development of a Minimally Invasive Screening Tool to Identify Obese Pediatric Population at Risk of Obstructive Sleep Apnea/Hypopnea Syndrome
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 d...
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doaj-3b3b9c47685c4149a42aadf2094a14f62020-11-25T03:44:29ZengMDPI AGBioengineering2306-53542020-10-01713113110.3390/bioengineering7040131Development of a Minimally Invasive Screening Tool to Identify Obese Pediatric Population at Risk of Obstructive Sleep Apnea/Hypopnea SyndromeJosé Miguel Calderón0Julio Álvarez-Pitti1Irene Cuenca2Francisco Ponce3Pau Redon4Fundación Investigación Hospital Clínico (INCLIVA), Avda. Menedez Pelayo 4, 46010 Valencia, SpainPediatric Department, Consorcio Hospital General Universitario de Valencia, Avda. Tres Cruces s/n, 46014 Valencia, SpainFundación Investigación Hospital Clínico (INCLIVA), Avda. Menedez Pelayo 4, 46010 Valencia, SpainPediatric Department, Consorcio Hospital General Universitario de Valencia, Avda. Tres Cruces s/n, 46014 Valencia, SpainPediatric Department, Consorcio Hospital General Universitario de Valencia, Avda. Tres Cruces s/n, 46014 Valencia, SpainObstructive 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.https://www.mdpi.com/2306-5354/7/4/131machine learningoxygen saturation signalobstructive sleep apnea syndromeobese pediatric population |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
José Miguel Calderón Julio Álvarez-Pitti Irene Cuenca Francisco Ponce Pau Redon |
spellingShingle |
José Miguel Calderón Julio Álvarez-Pitti Irene Cuenca Francisco Ponce Pau Redon Development of a Minimally Invasive Screening Tool to Identify Obese Pediatric Population at Risk of Obstructive Sleep Apnea/Hypopnea Syndrome Bioengineering machine learning oxygen saturation signal obstructive sleep apnea syndrome obese pediatric population |
author_facet |
José Miguel Calderón Julio Álvarez-Pitti Irene Cuenca Francisco Ponce Pau Redon |
author_sort |
José Miguel Calderón |
title |
Development of a Minimally Invasive Screening Tool to Identify Obese Pediatric Population at Risk of Obstructive Sleep Apnea/Hypopnea Syndrome |
title_short |
Development of a Minimally Invasive Screening Tool to Identify Obese Pediatric Population at Risk of Obstructive Sleep Apnea/Hypopnea Syndrome |
title_full |
Development of a Minimally Invasive Screening Tool to Identify Obese Pediatric Population at Risk of Obstructive Sleep Apnea/Hypopnea Syndrome |
title_fullStr |
Development of a Minimally Invasive Screening Tool to Identify Obese Pediatric Population at Risk of Obstructive Sleep Apnea/Hypopnea Syndrome |
title_full_unstemmed |
Development of a Minimally Invasive Screening Tool to Identify Obese Pediatric Population at Risk of Obstructive Sleep Apnea/Hypopnea Syndrome |
title_sort |
development of a minimally invasive screening tool to identify obese pediatric population at risk of obstructive sleep apnea/hypopnea syndrome |
publisher |
MDPI AG |
series |
Bioengineering |
issn |
2306-5354 |
publishDate |
2020-10-01 |
description |
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. |
topic |
machine learning oxygen saturation signal obstructive sleep apnea syndrome obese pediatric population |
url |
https://www.mdpi.com/2306-5354/7/4/131 |
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