Effect of Feature Extraction on Automatic Sleep Stage Classification by Artificial Neural Network
| Autor: | Adam G. Polak, Monika A. Prucnal |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2017 |
| Předmět: |
Discrete wavelet transform
Stage classification Computer science 0206 medical engineering Feature extraction 02 engineering and technology power spectral density lcsh:Technology Hilbert–Huang transform 0202 electrical engineering electronic engineering information engineering empirical mode decomposition discrete wavelet transform Artificial neural network EEG signal business.industry Time delay neural network lcsh:T sleep stage classification Spectral density Pattern recognition 020601 biomedical engineering 020201 artificial intelligence & image processing Artificial intelligence Sleep (system call) business artificial neural network |
| Zdroj: | Metrology and Measurement Systems, Vol 24, Iss 2, Pp 229-240 (2017) |
| ISSN: | 2300-1941 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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