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


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.

Publication DOI:
Divisions: ?? 50811700Jl ??
College of Engineering & Physical Sciences > Systems analytics research institute (SARI)
Additional Information: Copyright of the Institute of Physics
Uncontrolled Keywords: obstructive sleep apnoea syndrome (OSAS),nocturnal pulse oximetry,multilayer perceptron (MLP),maximum likelihood,Bayesian inference,Biophysics,Physiology,Physiology (medical)
Publication ISSN: 1361-6579
Last Modified: 15 Jul 2024 07:05
Date Deposited: 16 Mar 2010 12:35
Full Text Link: http://www.iop. ... -3334/31/3/007/
Related URLs: http://www.scop ... tnerID=8YFLogxK (Scopus URL)
PURE Output Type: Article
Published Date: 2010
Authors: Marcos, J.V.
Homero, R.
Álvarez, D.
Nabney, Ian T. (ORCID Profile 0000-0003-1513-993X)
del Campo, F.
Zamarron, C.


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