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oai:uvadoc.uva.es:10324/804602025-12-10T20:02:01Zcom_10324_1165com_10324_931com_10324_894col_10324_1335

00925njm 22002777a 4500 dc Marcos Martín, José Víctor author Hornero Sánchez, Roberto author Álvarez González, Daniel author Campo Matias, Félix del author López-Coronado Sánchez-Fortún, Miguel author Zamarrón, Carlos author 2008 The aim of this study is to assess the ability of multilayer perceptron (MLP) neural networks as an assistant tool in the diagnosis of the obstructive sleep apnoea syndrome (OSAS). Non-linear features from nocturnal oxygen saturation (SaO2) recordings were used to discriminate between OSAS positive and negative patients. A total of 187 subjects suspected of suffering from OSAS (111 with a positive diagnosis of OSAS and 76 with a negative diagnosis of OSAS) took part in the study. The initial population was divided into training, validation and test sets for deriving and testing our neural network classifier. Three methods were applied to extract non-linear features from SaO2 signals: approximate entropy (ApEn), central tendency measure (CTM) and Lempel-Ziv complexity (LZC). The selected MLP-based classifier provided a diagnostic accuracy of 85.5% (89.8% sensitivity and 79.4% specificity). Our neural network algorithm could represent a useful technique for OSAS detection. It could contribute to reduce the demand for polysomnographic studies in OSAS screening. Marcos, J.V., Hornero, R., Álvarez, D., Del Campo, F., Zamarrón, C. and López, M., 2008. Utility of multilayer perceptron neural network classifiers in the diagnosis of the obstructive sleep apnoea syndrome from nocturnal oximetry. Computer Methods and Programs in Biomedicine, 92(1), pp.79-89. 0169-2607 https://uvadoc.uva.es/handle/10324/80460 10.1016/j.cmpb.2008.05.006 79 1 89 Computer Methods and Programs in Biomedicine 92 Utility of multilayer perceptron neural network classifiers in the diagnosis of the obstructive sleep apnoea syndrome from nocturnal oximetry