Paper Info
Title
Improving the Diagnostic Ability of Oximetry Recordings in Pediatric Sleep Apnea-Hypopnea Syndrome by Means of Multi-Class AdaBoost
Abstract
Pediatric sleep apnea-hypopnea syndrome (SAHS) is a highly prevalent respiratory disorder that may impose many negative effects on the health and development of children. Due to the drawbacks of overnight polysomnography (PSG), the gold standard diagnosis technique, automated analysis of nocturnal oximetry has emerged as a simplified alternative. In order to improve diagnosis ability of oximetry, we propose to evaluate the usefulness of AdaBoost, a classification boosting algorithm, in the context of pediatric SAHS. A database composed of 981 SpO <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> recordings from pediatric subjects was used. For this purpose, a signal processing approach divided into two main stages was conducted: (i) feature extraction, where 3% oxygen desaturation index (ODI3), spectral, and nonlinear features were computed from the oximetry signal, and (ii) AdaBoost classification, where an AdaBoost.M2 model was trained with these features in order to determine the severity of pediatric SAHS according to the apnea-hypopnea index (AHI): AHI<;1 events per hour (e/h), 1≤AHI<;5 e/h, and AHI≥5 e/h. Our AdaBoost.M2 model achieved a Cohen's kappa of 0.474 in an independent test set in the 3-class classification task. In addition, high accuracies were obtained when using the AHI cutoffs for diagnosis of mild (AHI=1 e/h) and moderate-to-severe (AHI=5 e/h) SAHS: 80.9% and 82.9%, respectively. These results achieved slightly higher diagnostic accuracies than ODI3 as well as state-of-the-art studies. Therefore, AdaBoost could help to enhance the diagnostic ability of the oximetry signal to assess pediatric SAHS severity.
Year
DOI
Venue
2018
10.1109/EMBC.2018.8512264
2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)
Keywords
Field
DocType
Algorithms,Blood Gas Analysis,Child,Databases, Factual,Female,Humans,Male,Oximetry,Polysomnography,Prevalence,Records,Signal Processing, Computer-Assisted,Sleep Apnea, Obstructive
Computer vision,Kappa,Sleep apnea,AdaBoost,Internal medicine,Computer science,Cardiology,Boosting (machine learning),Artificial intelligence,Statistical classification,Sleep Apnea Hypopnea Syndrome,Polysomnography
Conference
Volume
ISSN
ISBN
2018
1557-170X
978-1-5386-3647-3
Citations 
PageRank 
References 
0
0.34
3
Authors
9
Name
Order
Citations
PageRank
Fernando Vaquerizo-Villar166.92
Daniel Álvarez221225.22
Leila Kheirandish-Gozal344.09
Gonzalo C. Gutiérrez-Tobal43110.29
Verónica Barroso-García566.59
Andrea Crespo653.47
Félix del Campo712618.12
David Gozal844.42
Roberto Hornero960367.74