Automated Sleep Stage Analysis and Classification Based on Different Age Specified Subjects From a Single-Channel of EEG Signal

The present work mainly focused on automatic classification of the sleep stages classification from the different medical-conditioned subjects under American Academy of Sleep Medicine (AASM). The present research study was used single individual channel for classifying two-state sleep stage classification problem in between wake versus Sleep. During first part, we have conducted experiments such as acquisition of data from participated subjects, preprocessing the raw signal to remove the irrelevant artefacts and muscle movements from recorded sleep data, extract quantitative features obtained from EEG signal and also used feature selection techniques to choose the suitable features which is most useful for characterizing the abnormality pattern. In second step, we have used two machine learning classifiers such as support vector machine (SVM) and K-nearest neighbor (KNN). The proposed scheme was conducted through six different medical conditions subjects with considering both affected sleep diseases subjects and healthy subjects and their one session and two session recordings. The obtained results demonstrated that the proposed methodologies support to sleep experts for accurately measure the irregularities occurred during sleep and also helps the clinicians to evaluate the presence and criticality of sleep related disorders.