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Obstructive Sleep Apnea Detection Using Clustering Classification of Nonlinear Features from Nocturnal Oximetry

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5 Author(s)
Daniel Alvarez ; Student Member, IEEE, Biomedical Engineering Group, E.T.S. Ingenieros de Telecomunicación, University of Valladolid, Camino del Cementerio s/n, 47011, Valladolid, Spain. phone: +34 983 423000, ext. 5589, fax: +34 983 423667; e-mail: dalvgon@ribera.tel.uva.es ; Roberto Hornero ; J. Victor Marcos ; Felix del Campo
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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.

Published in:

2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society

Date of Conference:

22-26 Aug. 2007