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Classification of Sleep Stages Based on EEG Signals

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The sleep stage classification is one of methods the most important for the diagnosis   in psychiatry and in neurology. This paper presents a new algorithm for sleep stages classification using the Electroencephalogram (EEG) signals.  After preprocessing, we extracted eight spectral features, three temporal features, and a nonlinear feature from each EEG segment. For the classification, we propose a neural network algorithm called Extreme Learning Machine (ELM) which has a fast learning speed and high accuracy. To investigate the accuracy of the proposed methods, we conduct experiments using a MIT_BIH polysomnographic database. Three set of classification output was used to differentiate six sleep stages (Wake, S1, S2, S3, S4, and REM), four sleep stages (Wake, S1+REM, S2, and S3+S4) and two sleep stages (Wake and Sleep). The experimental results show that the overall  classification accuracy of the proposed algorithm are 89.15% using 2 output classes, 66.86 % using 4 output classes, and 56.81 % using 6 output classes.
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EEG; Sleep Stages; Extreme Learning Machine

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