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

Abstract

Background: Sleep disorders become one of the early warnings of Non-Communicable Diseases (NCDs).In sleep stage classification process one of the important stages to be sleep score recording. Generally the first step of diagnosis of sleep disorder is to be Polysomnography (PSG) test.The PSG test is a formal method to diagnose sleep disorders, during this test we have considered many biomedical signals such as electroencephalogram (EEG),electrooculogram (EOG) and electromyogram (EMG).Sleep Stage classification (SSC) process is time taking and there must be a presence of sleep experts or technicians definitely to be stay with subject through the whole recording time period, which is somehow overburden for clinicians and it may hamper sometime to record the correct results from subjects. For that reason now researchers obtained Automatic Sleep Stage Classification (ASSC) methods in order to find disturbances during sleep and it’s quite faster and efficient in towards data recording accuracy from PSG signals.Method: In this study, we have proposed an alternative approach for sleep stage scoring by considering different age subjects from same gender with their optimal features. In this proposed study we have considered dual channels of EEG signals such as C4-A1 and O2-A1. In data pre-processing stage, the datasets were analyzed and normalized using feature extraction and feature selection methods. The main important part of this research work is to be comparing between dual channels and its accuracy of best discrimination in between wake and sleep stages. In addition, we have obtained three base classifier such as support vector machines (SVM), decision tree (DT) and K-nearest neighbors (KNN). In addition we have also adopted ensemble classifier (Boosting), to make a proper comparison the classification performances in between them. For validation purpose in between training data and test data, we have used 10-fold cross validation techniques.Results: In this study, we have made a comparison the performances in terms channel effectiveness and classification algorithm effectiveness with regard to discriminate in between the sleep stages. As per outcome from the proposed system the SVM classification techniques achieved best accuracy in comparable to other classifiers.Regading to channel effectiveness, C4-A1 recording is more appropriate for sleep stage scoring.Discussion and Conclusion: As per the related research work in this field , the introduced approach in the present study achieved an acceptable performance in sleep scoring in order to classifying wake stage and sleep stage from dual channels of EEG signals. Our experiment design compares the accuracy of classification in between two channels and find out which channel recordings and classification techniques to be most effective in towards classifying sleep stages. In future its performance to be increase through proper enhancing through different intelligent techniques in related to process of diagnosing and treatment of sleep disorders.