A Multi-Model Fast Denoising Method Based on the Wavelet Transform Threshold Denoising

The biomedical signals are often corrupted by noise in their acquisition or transmission resulting in lower Signal to Noise Ratio (SNR), which brings problematic obstacles to successive biomedical signal processing. So suppressing noise and improving SNR effectively is an essential procedure and key issue in the research on biomedical signal processing. In this paper, we propose a novel multi-model fast denoising method based on the Wavelet transform threshold denoising. The proposed denoising scheme not only solves the Pseudo-Gibbs phenomenon to filter the signal effectively but also preserves the signal details to retain the diagnostic information. Meanwhile, the summed data processing method is advanced to realize the fast denoising. The simulation experiments on electrocardiogram(ECG) indicate that the proposed method can effectively and quickly separate signal from noise.