Multivariate Analysis of Blood Oxygen Saturation Recordings in Obstructive Sleep Apnea Diagnosis

• Published in: IEEE transactions on biomedical engineering 2010-12, Vol.57 (12), p.2816-2824
• Main Authors: Alvarez, Daniel, Hornero, Roberto, Victor Marcos, J., del Campo, Felix
• Format: Article
• Language: English
• Subjects: Adult; Aged; Algorithms; Biological and medical sciences; Blood; Computerized, statistical medical data processing and models in biomedicine; Costs; Female; Frequency domain analysis; Humans; Information analysis; Logistic Models; logistic regression (LR); Logistics; Male; Medical management aid. Diagnosis aid; Medical sciences; Middle Aged; Multivariate Analysis; nocturnal pulse oximetry (NPO); nonlinear analysis; obstructive sleep apnea (OSA); Oximetry - methods; Oxygen; Oxygen - blood; Permission; Pneumology; Polysomnography - methods; Reproducibility of Results; Respiratory system : syndromes and miscellaneous diseases; Sleep apnea; Sleep Apnea, Obstructive - blood; Sleep Apnea, Obstructive - diagnosis; Sleep disorders; Statistics; Time domain analysis
• ISSN: 0018-9294; 1558-2531
• DOI: 10.1109/TBME.2010.2056924

This study focuses on the analysis of blood oxygen saturation (SaO 2 ) from nocturnal pulse oximetry (NPO) to help in the diagnosis of the obstructive sleep apnea (OSA) syndrome. A population of 148 patients suspected of suffering from OSA syndrome was studied. A wide set of 16 features was used to characterize changes in the SaO 2 profile during the night. Our feature set included common statistics in the time and frequency domains, conventional spectral characteristics from the power spectral density (PSD) function, and nonlinear features. We performed feature selection by means of a step-forward logistic regression (LR) approach with leave-one-out cross-validation. Second- and fourth-order statistical moments in the time domain (M2t and M4t), the relative power in the 0.014-0.033 Hz frequency band (P R ), and the Lempel-Ziv complexity (LZC) were automatically selected. 92.0% sensitivity, 85.4% specificity, and 89.7% accuracy were obtained. The optimum feature set significantly improved the diagnostic ability of each feature individually. Furthermore, our results outperformed classic oximetric indexes commonly used by physicians. We conclude that simultaneous analysis in the time and frequency domains by means of statistical moments, spectral and nonlinear features could provide complementary information from NPO to improve OSA diagnosis.