Título del libro: Conielecomp 2011 - 21st International Conference On Electronics Communications And Computers, Proceedings
Título del capítulo: Wavelet-based EEG denoising for automatic sleep stage classification
EDSON BALTAZAR ESTRADA ARRIAGA;
Autores externos:
Idioma:
Inglés
Año de publicación:
2011
Palabras clave:
denoising; EEG signals; Effective solution; Minimum squared error; Nonstationary signals; Performance measure; Pre-processing step; Shrinkage properties; Signal to noise; Sleep stage; Soft thresholding; Thresholding; Universal threshold; Wavelet coefficients; Wavelet denoising method; Wavelets; Discrete wavelet transforms; Signal processing; Signal to noise ratio; Sleep research; Noise pollution control
Resumen:
In automatic sleep stage classification, as in any other signal processing task involving the easily contaminated EEG signals, denoising constitutes a crucial pre-processing step that must be addressed before carrying out further analysis on the EEG signals. Discrete wavelet transform offers an effective solution for denoising nonstationary signals such as EEG due to its shrinkage property. In this paper, we explored the application of wavelet denoising method to EEG signals acquired during different sleep stages classified according to the RK rules, with the objective to identify suitable thresholding rules and threshold values. Preliminary results showed that the combination of soft thresholding rule applied to the Detailed wavelet coefficients with the Universal threshold value produced better performance measures such as a smaller Minimum Squared Error (MSE) and a larger signal-to-Noise Ratio (SNR). Similarly improved results were obtained for Stage 1, Stage 2, Stage 3, Stage 4 and REM stage EEG signals using this combination. Such thresholding rule and values are equally well applicable to denoising EEG epochs acquired from deep sleep stages. © 2011 IEEE.
Entidades citadas de la UNAM:
ISBN:
9781424495573
Editorial:
IEEE Computer Society
