Hierarchical Multi-agent System for Sleep Stages Classification

Abstract

Sleep is a fundamental restorative process for human mental and physical health. Considering the risks that sleep disorders can present, sleep analysis is considered as a primordial task to identify the different abnormalities. Sleep scoring is the gold standard for human sleep analysis. The manual sleep scoring task is considered exhausting, subjective, time-consuming and error prone. Moreover, sleep scoring is based on fixed epoch lengths usually of 30 seconds, which leads to an information loss problem. In this paper, we propose an automatic unsupervised sleep scoring model. The aim of our work is to consider different epoch’s durations to classify sleep stages. Therefore, we developed a model based on Hierarchical Multi-Agent Systems (HMASs) that presents different layers where each layer contains a number of adaptive agents working with a specific time epoch. The effectiveness of our approach was investigated using real electroencephalography (EEG) data. Good results were reached according to a comparative study realized with the often used machine learning techniques for sleep stages classification problems.