Document Type : Full Research Paper


1 Ph.D. Student, Biomedical Engineering Department, Faculty of Electrical and Computer Engineering, University of Tabriz, Tabriz, Iran

2 Associate Professor, Faculty of Electrical and Computer Engineering, University of Tabriz, Tabriz, Iran


The automatic classification of sleep stages is essential for the timely detection of disorders and sleep-related studies. In this paper, a single-channel EEG-based algorithm is used to automatically identify sleep stages using discrete wavelet transform and a hybrid model of ant colony optimizer and neural network based on RUSBoost. The signal is decomposed using a discrete wavelet transform into four levels and statistical properties of each level are calculated. To optimize and reduce the dimensions of feature vectors, hybrid model of ant colony optimizer algorithm and multi-layered neural network are used. Then ANOVA test is applied to validate the selected features. Finally, the classification is performed on RUSBoost, which provides an average of 90% classification accuracy for 2 to 6-class classification of different steps of sleep EEG. Suggesting that the proposed method has a higher degree of success in classifying sleep stages compared to the existing methods.


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