Classifying Sleep Apneas Using Neural Networks and a Combination of Experts

Abstract

Objective: The involuntary periodic repetition of respiratory pauses or apneas constitutes the sleep apnea-hypopnea syndrome (SAHS). This paper presents two novel approaches for sleep apnea classification in one of their three basic types: obstructive, central and mixed. The goal is to improve the classification accuracy obtained in previous works. Materials and methods: Both models are based on a combination of classifiers whose inputs are the coefficients obtained by a discrete wavelet decomposition applied to the raw samples of the apnea in the thoracic effort signal. The first model builds adaptive data-dependent committees, subsets of classifiers that are specifically selected for each input pattern. The second one uses a new classification approach based on the characteristics each type of apnea presents in different segments of the apnea. This model is based on the Error Correcting Output Code and its input coefficients were determined by a feature selection method (SVM Recursive Feature Elimination). In order to train and test the systems, 120 events from six different patients were used. The true error rate was estimated using 10 different simulations of a 10-fold cross validation. The mean test accuracy, obtained over the test set was 85.20%±1.25 for the first model and 90.27%±0.79 for the second one. The proposed classifiers surpass, up to the author’s knowledge, other previous results. Moreover, the results achieved are correctly enough to obtain a reliable diagnosis of SAHS, taking into account the average duration of a sleep test and the number of apneas presented for a patient who suffers SAHS.