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Updated: Jul 10, 2026

Drug-Induced Sleep Endoscopy (DISE) with Target Controlled Infusion (TCI) and Bispectral Analysis in Obstructive Sleep Apnea
Published on: December 6, 2016
Obstructive sleep apnea detection using clustering classification of nonlinear features from nocturnal oximetry
Daniel Alvarez1, Roberto Hornero, J Víctor Marcos
1Biomedical Engineering Group, ETS Ingenieros de Telecomunicación, University of Valladolid, Camino del Cementerio s/n, 47011, Valladolid, Spain. dalvgon@ribera.tel.uva.es
Abstract:
This study is focused on the classification of patients suspected of suffering from obstructive sleep apnea (OSA) by means of cluster analysis. We assessed the diagnostic ability of three clustering algorithms: k-means, hierarchical and fuzzy c-means (FCM). Nonlinear features of blood oxygen saturation (SaO2) from nocturnal oximetry were used as inputs to the clustering methods. Three nonlinear methods were used: approximate entropy (ApEn), central tendency measure (CTM) and Lempel-Ziv (LZ) complexity. A population of 74 subjects (44 OSA positive and 30 OSA negative) was studied. 90.5%, 87.8% and 86.5% accuracies were reached with k-means, hierarchical and FCM algorithms, respectively. The diagnostic accuracy values improved those obtained with each nonlinear method individually. Our results suggest that nonlinear analysis and clustering classification could provide useful information to help in the diagnosis of OSA syndrome.
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