Nonlinear Characteristics of Blood Oxygen Saturation from Nocturnal Oximetry for Obstructive Sleep Apnoea Detection
Overview
Biophysics
Physiology
Authors
Affiliations
Nocturnal oximetry is an attractive option for the diagnosis of obstructive sleep apnoea (OSA) syndrome because of its simplicity and low cost compared to polysomnography (PSG). The present study assesses nonlinear analysis of blood oxygen saturation (SaO(2)) from nocturnal oximetry as a diagnostic test to discriminate between OSA positive and OSA negative patients. A sample of 187 referred outpatients, clinically suspected of having OSA, was studied using nocturnal oximetry performed simultaneously with complete PSG. A positive OSA diagnosis was found for 111 cases, while the remaining 76 cases were classified as OSA negative. The following oximetric indices were obtained: cumulative time spent below a saturation of 90% (CT90), oxygen desaturation indices of 4% (ODI4), 3% (ODI3) and 2% (ODI2) and the delta index (Delta index). SaO(2) records were subsequently processed applying two nonlinear methods: central tendency measure (CTM) and Lempel-Ziv (LZ) complexity. Significant differences (p < 0.01) were found between OSA positive and OSA negative patients. Using CTM we obtained a sensitivity of 90.1% and a specificity of 82.9%, while with LZ the sensitivity was 86.5% and the specificity was 77.6%. CTM and LZ accuracies were higher than those provided by ODI4, ODI3, ODI2 and CT90. The results suggest that nonlinear analysis of SaO(2) signals from nocturnal oximetry could yield useful information in OSA diagnosis.
Ensemble-learning regression to estimate sleep apnea severity using at-home oximetry in adults.
Gutierrez-Tobal G, Alvarez D, Vaquerizo-Villar F, Crespo A, Kheirandish-Gozal L, Gozal D Appl Soft Comput. 2024; 111.
PMID: 39544517 PMC: 11563155. DOI: 10.1016/j.asoc.2021.107827.
Deep Attention Networks With Multi-Temporal Information Fusion for Sleep Apnea Detection.
Jiao M, Song C, Xian X, Yang S, Liu F IEEE Open J Eng Med Biol. 2024; 5:792-802.
PMID: 39464487 PMC: 11505982. DOI: 10.1109/OJEMB.2024.3405666.
Predicting Alzheimer's disease CSF core biomarkers: a multimodal Machine Learning approach.
Gaeta A, Quijada-Lopez M, Barbe F, Vaca R, Pujol M, Minguez O Front Aging Neurosci. 2024; 16:1369545.
PMID: 38988328 PMC: 11233742. DOI: 10.3389/fnagi.2024.1369545.
Paul T, Hassan O, Islam S, Mosa A AMIA Jt Summits Transl Sci Proc. 2024; 2024:662-669.
PMID: 38827094 PMC: 11141842.
Sullivan B, Beam K, Vesoulis Z, Aziz K, Husain A, Knake L J Perinatol. 2023; 44(1):1-11.
PMID: 38097685 PMC: 10872325. DOI: 10.1038/s41372-023-01848-5.