Chapter 7 - A study on sleep stage classification based on a single-channel EEG signal

Abstract

Automated sleep stage scoring can deliver a clinical report for analyzing patients with sleep abnormalities. Traditional polysomnography or visual scoring is unfit for observing the large populations since visual scoring is extremely conservative and depends on skillful knowledge based on Rechtschaffen and Kales score and American Academy of Sleep Medicine rules. This chapter presents a study on single-channel EEG (electroencephalogram) signals for finding the fittest approach in terms of preprocessing, extraction of features, and classification for sleep abnormalities analysis. It focuses on the various applications regarding the importance of the prediction of sleep stage scoring. Analysis of the sleep stage based on the qualitative method along with traditional quantitative methods is presented. The datasets for sleep abnormality analysis based on EEG signals are introduced in this chapter. Also, the employed expert rules and relevant character of the EEG signal dataset play significant roles in the study as well. In this work, various state-of-the-art methods are comprehensively reviewed based on all of the significant features discussed here. Each step in the sleep stage scoring is briefly explained, and the relevant investigations are presented.