Uyku evreleri analizi

In this project, methods for extraction features and classification for sleep stage using EEG (Electroencephalogram) are presented. The algorithm consists of basically two modules, feature extraction and classification. Firstly, features used in the classification step are extracted. In the beginning of feature extraction, segmentation is applied for breaking down the signal into fixed sections to match with the labels. Then the parameters are extracted from these fixed sections. The extracted features are the parameters of Hjorth, harmonic parameters, the relative band energy ratios and the parameters that are obtained by applying wavelet packet transformation (WPT). Then all these extracted features are used in the classifiers which are constructed by using k-nearest neighbor, multilayer neural network and linear discriminant analysis.