Abstract
In this paper a method for sleep apnea classification is proposed. The method is based on a feedforward neural network trained using a bayesian framework and a cross-entropy error function. The inputs of the neural network are the first level-5-detail coefficients obtained from a discrete wavelet transformation of the samples of the thoracic effort signal corresponding to the apnea. In order to train and validate the presented method, 120 events from 6 different patients were used. The true error rate was estimated using a 10-fold cross validation. The presented results were averaged over 100 different simulations and a multiple comparison procedure was used to model selection. The mean classification accuracy obtained over the test set was 83.78%±1.90.
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Fontenla-Romero, O., Guijarro-Berdiñas, B., Alonso-Betanzos, A., del Rocío Fraga-Iglesias, A., Moret-Bonillo, V. (2003). A Bayesian Neural Network Approach for Sleep Apnea Classification. In: Dojat, M., Keravnou, E.T., Barahona, P. (eds) Artificial Intelligence in Medicine. AIME 2003. Lecture Notes in Computer Science(), vol 2780. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-39907-0_39
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DOI: https://doi.org/10.1007/978-3-540-39907-0_39
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