Improving biosignal processing through modeling uncertainty: bayes vs. Non-bayes in sleep staging

摘要

In this paper about an investigation of Bayesian inference applied to neural networks-multilayer perceptrons (MLP), in particular-in the task of automatic sleep staging based on electroencephalogram (EEG) and electrooculogram (EOG) signals. The main focus was on evaluating the use of so-called "doubt-levels" and "confidence intervals" ("error bars") in improving the results by rejecting uncertain cases and patterns not well represented by the training set. Bayesian inference is used to arrive at distributions of network weights based on training data. We compare the results of the full-blow Bayesian method with results obtained from a k-nearest neighbor classifier.