Effect of Feature Extraction on Automatic Sleep Stage Classification by Artificial Neural Network

• Autorzy: Prucnal M. , Polak A. G.

Identyfikatory

DOI

10.1515/mms-2017-0036

• Języki publikacji: EN

Abstrakty

EN

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.

Słowa kluczowe

EN

sleep stage classification; EEG signal; power spectral density; discrete wavelet transform; empirical mode decomposition; artificial neural network

• Wydawca: Komitet Metrologii i Aparatury Naukowej PAN
• Czasopismo: Metrology and Measurement Systems
• Rocznik: 2017
• Tom: Vol. 24, nr 2
• Strony: 229--240
• Opis fizyczny: Bibliogr. 49 poz., rys., tab., wykr., wzory

Twórcy

autor

Prucnal M.

• Wrocław University of Science and Technology, Faculty of Electronics, B. Prusa 53/55, Wrocław, Poland

autor

Polak A. G.

• Wrocław University of Science and Technology, Faculty of Electronics, B. Prusa 53/55, Wrocław, Poland

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Uwagi

PL

Opracowanie ze środków MNiSW w ramach umowy 812/P-DUN/2016 na działalność upowszechniającą naukę (zadania 2017).