Title :
A Multi-Model Fast Denoising Method Based on the Wavelet Transform Threshold Denoising
Author :
Wang, Feng ; Wang, Chendi ; Feng, Nan
Author_Institution :
Sch. of Biol. Sci. & Med. Eng., Southeast Univ., Nanjing, China
Abstract :
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.
Keywords :
electrocardiography; medical signal processing; signal denoising; wavelet transforms; biomedical signal processing; electrocardiogram; multimodel fast denoising method; pseudo Gibbs phenomenon; signal to noise ratio; wavelet transform threshold denoising; Electrocardiography; Filtering; Noise; Noise reduction; Wavelet analysis; Wavelet transforms; Wavelet denoisin; biomedical signal; multi-model method; summed data; threshold function;
Conference_Titel :
Complexity and Data Mining (IWCDM), 2011 First International Workshop on
Conference_Location :
Nanjing, Jiangsu
Print_ISBN :
978-1-4577-2007-9
DOI :
10.1109/IWCDM.2011.9