A COMBINATION DEEP BELIEF NETWORKS AND SHALLOW CLASSIFIER FOR SLEEP STAGE CLASSIFICATION

Abstract

In this research, it is proposed to use Deep Belief Networks (DBN) in shallow classifier for the automatic sleep stage classification. The automatic classification is required to minimize Polysomnography examination time because it needs more than two days for analysis manually. Thus the automatic mechanism is required. The Shallow classifier used in this research includes Naïve Bayes (NB), Bayesian Networks (BN), Decision Tree (DT), Support Vector Machines (SVM), and K-Nearest Neighbor (KNN). The results obtained that many methods of the shallow classifier are increasing precision, recall, and F-Measure if they use DBN output as input for classification. Experiments that have been done indicate a significant increase of Naive Bayes after being combined with DBN. The high-level features generated by DBN are proven to be useful in helping Naive Bayes' performance. On the other hand, the combination of KNN with DBN shows a decrease because high-level features of DBN make it harder to find neighbors that optimize the performance of KNN.