@ARTICLE{Prucnal_Monika_Effect_2017, author={Prucnal, Monika and Polak, Adam G.}, volume={vol. 24}, number={No 2}, journal={Metrology and Measurement Systems}, pages={229–240}, howpublished={online}, year={2017}, publisher={Polish Academy of Sciences Committee on Metrology and Scientific Instrumentation}, abstract={EEG signal-based sleep stage classification facilitates an initial diagnosis of sleep disorders. The aim of this study was to compare the efficiency of three methods for feature extraction: power spectral density (PSD), discrete wavelet transform (DWT) and empirical mode decomposition (EMD) in the automatic classification of sleep stages by an artificial neural network (ANN). 13650 30-second EEG epochs from the PhysioNet database, representing five sleep stages (W, N1-N3 and REM), were transformed into feature vectors using the aforementioned methods and principal component analysis (PCA). Three feed-forward ANNs with the same optimal structure (12 input neurons, 23 + 22 neurons in two hidden layers and 5 output neurons) were trained using three sets of features, obtained with one of the compared methods each. Calculating PSD from EEG epochs in frequency sub-bands corresponding to the brain waves (81.1% accuracy for the testing set, comparing with 74.2% for DWT and 57.6% for EMD) appeared to be the most effective feature extraction method in the analysed problem.}, type={Artykuły / Articles}, title={Effect of Feature Extraction on Automatic Sleep Stage Classification by Artificial Neural Network}, URL={http://www.journals.pan.pl/Content/107361/PDF/151.pdf}, doi={10.1515/mms-2017-0036}, keywords={sleep stage classification, EEG signal, power spectral density, discrete wavelet transform, empirical mode decomposition, artificial neural network}, }