Abstract:

To deal with the problems that the weak physiological signals are easily drowned by noise and the poor denoising effect and signal extraction distortion exist in traditional wavelet denoising method, an improved wavelet threshold denoising method based on the energy distribution characteristics of wavelet coefficients is proposed. The threshold is determined by calculating the energy of wavelet coefficient in each layer, which avoids the imbalance of threshold calculation while improving the adaptivity and the fidelity of weak signals. An improved adjustable two-parameter threshold function is used to process the wavelet coefficients, which allows the changing trend of threshold function to be freely adjusted while the degree of compression of the wavelet coefficients is controlled. The experimental results show that the improved wavelet threshold denoising method has more obvious advantages in signal-to-noise ratio, root-mean-square percentage and root-mean-square error compared with two traditional denoising algorithms (the empirical modal decomposition algorithm and the filter algorithm) and 12 traditional algorithms for combining wavelet thresholding and thresholding functions, and achieves the smallest average relative error and the smallest volatility in the measured physiological signal.

Key words: wavelet transform, energy gradient threshold, improved threshold function, physiological signal, denoising