Wavelet-based EEG denoising for automatic sleep stage classification

Abstract

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.