Sleep Stage Classification by Ensemble Learning Methods with Active Sample Selection Techniques

Abstract

In medical science, sleep stages are the main criteria to define the disorders and have crucial role on diagnostic. In this sense, accurate sleep stage classification plays important role due to provide better report on medications and diagnoses. In this study, EEG signals are classified by a rule based machine learning algorithm; Decision Tree with the ensemble and classical machine learning idea. Additionally, two of active sample selection technique using the idea of strictly separated discrimination and margin distances are applied on learning processes to obtain more accurate results with less samples. This paper proves that ensemble learning algorithms with one of the implemented active sample selection technique gives more successful result on the determination of stages.