Sleep stage detection using chaotic feature analysis of Electroencephalography (EEG) signals

The present study was conducted to detect the sleep stages by electroencephalography (EEG) using chaotic features. The method used in this study was the content analysis method. First, the sleep stages and EEG have been analyzed, and the EEG with chaotic features was used to detect the sleep stages. Detection of artifacts in sleep electroencephalography (EEG) is one of the vital tasks in the pre-processing stage. Despite many artifact exploration algorithms over the years, lots of them lose their advantages to use sleep EEG. Types of brain activities can be measured, and the involved brain areas can be detected using EEG. Electroencephalography (EEG) signal includes different rhythms, which are dependent on various sensory and movement conditions. Detection of each rhythm of this signal needs experience and skills. As a result, analysis of the signal recorded by EEG can be used widely for detection and academic purposes.