Abstract
Human’s sleep is divided into two segments, Rapid Eye Movement (REM) sleep and Non-REM (NREM) sleep. NREM sleep is further divided into 4 stages. Sleep staging attempts to identify these stages based on the signals collected in polysomnogram (PSG). Significant information can be derived from the EEG signals collected during PSG.
In our study we extract spectral features from EEG signals. Genetic Algorithm (GA) is used for selecting best features and then Support Vector Machine (SVM) with different kernels have been applied to differentiate sleep stages. According to chaotic characteristic of EEG signal, we use non-linear features, as well. Nonlinear and spectral features can differentiate stages awake and REM with 98.15% accuracy. Chaotic features improved classification rate as a necessary component. Succinctly, through the feature space constructed by approximate entropy and fractal dimension, different stages of EEG signals can be recognized from each other expressly. That is to say, Pattern varies under the different sleep stages. Therefore Healthy humans with a regular night’s sleep will follow these sleep stages in a particular pattern.
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Vatankhah, M., Akbarzadeh Totonchi, M.R., Moghimi, A., Asadpour, V. (2010). GA/SVM for Diagnosis Sleep Stages Using Non-linear and Spectral Features. In: Gao, XZ., Gaspar-Cunha, A., Köppen, M., Schaefer, G., Wang, J. (eds) Soft Computing in Industrial Applications. Advances in Intelligent and Soft Computing, vol 75. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-11282-9_19
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DOI: https://doi.org/10.1007/978-3-642-11282-9_19
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