Classification of oximetry signals using Bayesian neural networks to assist in the detection of obstructive sleep apnoea syndrome

Research output: Contribution to journalArticle

View graph of relations Save citation


Research units


In the present study, multilayer perceptron (MLP) neural networks were applied to help in the diagnosis of obstructive sleep apnoea syndrome (OSAS). Oxygen saturation (SaO2) recordings from nocturnal pulse oximetry were used for this purpose. We performed time and spectral analysis of these signals to extract 14 features related to OSAS. The performance of two different MLP classifiers was compared: maximum likelihood (ML) and Bayesian (BY) MLP networks. A total of 187 subjects suspected of suffering from OSAS took part in the study. Their SaO2 signals were divided into a training set with 74 recordings and a test set with 113 recordings. BY-MLP networks achieved the best performance on the test set with 85.58% accuracy (87.76% sensitivity and 82.39% specificity). These results were substantially better than those provided by ML-MLP networks, which were affected by overfitting and achieved an accuracy of 76.81% (86.42% sensitivity and 62.83% specificity). Our results suggest that the Bayesian framework is preferred to implement our MLP classifiers. The proposed BY-MLP networks could be used for early OSAS detection. They could contribute to overcome the difficulties of nocturnal polysomnography (PSG) and thus reduce the demand for these studies.


  • PM_jvmarcos_ITN.pdf

    Rights statement: Copyright of the Institute of Physics

    468 KB, PDF-document


Original languageEnglish
Pages (from-to)375-394
Number of pages20
JournalPhysiological Measurement
Issue number3
Publication statusPublished - 2010

Bibliographic note

Copyright of the Institute of Physics


  • obstructive sleep apnoea syndrome (OSAS), nocturnal pulse oximetry, multilayer perceptron (MLP), maximum likelihood, Bayesian inference

Download statistics

No data available

Employable Graduates; Exploitable Research

Copy the text from this field...