Single layer network classifiers to assist in the detection of obstructive sleep apnea syndrome from oximetry data

The aim of this study is to assess the utility of single layer network classifiers to help in the diagnosis of the obstructive sleep apnea syndrome (SAOS). Oxygen saturation (SaO(2)) recordings from a total of 157 subjects suspected of suffering from OSAS were used. These were divided into a training set and a test set with 74 and 83 subjects, respectively. Four classification schemes were developed by using generalized linear models (GLM). Two GLM classifiers were built with spectral (GLM-SP) and non-linear (GLM-NL) features from SaO(2) signals, respectively. In addition, both algorithms were combined in order to improve their classification capability. The performance of two different ensemble classifiers was analyzed. The highest diagnostic accuracy was reached by the GLM-SP classifier (88%). The ensemble built from the combination of GLM-SP and GLM-NL by means of an additional GLM structure provided the best sensitivity value (87.8%). Applying spectral and non-linear features from SaO(2) data simultaneously could be useful in OSAS diagnosis.