Anaesthetic EEG signal denoise using improved nonlocal mean methods

Abstract

This paper applies the nonlocal mean (NLM) method to denoise the simulated and real electroencephalograph signals. As a patch-based method, the NLM method calculates the weighted sum of a patch. The weight of each point is determined by the similarity between the points of the own patch and its neighbor. Based on the weighted sum, the noise is filtered out. In this study, the NLM denoising method is applied to signals with additive Gaussian white noise, spiking noise and specific frequency noise and the results are compared with that of the popular sym8 and db16 Wavelet threshold denoising (WTD) methods. The outcomes show that the NLM on average achieves 2.70 dB increase in improved signal to noise ratio (SNRimp) and 0.37 % drop in improved percentage distortion ratio compared with WTD. The moving adaptive shape patches-NLM performs better than the original NLM when the signals change dramatically. In addition, the performance of combined NLMWTD denoising method is also better than original WTD method (0.50-4.89 dB higher in SNRimp), especially, when the signal quality is poor.