Obstructive sleep apnea detection using clustering classification of nonlinear features from nocturnal oximetry.
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.
Alvarez
Daniel
D
Biomedical Engineering Group, ETS Ingenieros de Telecomunicación, University of Valladolid, Camino del Cementerio s/n, 47011, Valladolid, Spain. dalvgon@ribera.tel.uva.es
Hornero
Roberto
R
Marcos
J Víctor
JV
del Campo
Félix
F
López
Miguel
M
D000368:Aged; D000465:Algorithms; D016000:Cluster Analysis; D003627:Data Interpretation, Statistical; D004867:Equipment Design; D005260:Female; D006801:Humans; D008297:Male; D008875:Middle Aged; D015233:Models, Statistical; D017711:Nonlinear Dynamics; D010092:Oximetry / Q000295:instrumentation* / Q000379:methods; D015203:Reproducibility of Results; D012890:Sleep; D020181:Sleep Apnea, Obstructive / Q000175:diagnosis* / Q000473:pathology*