title: Application of the dual-tree complex wavelet transform in biomedical signal denoising.
abstract: In biomedical signal processing, Gibbs oscillation and severe frequency aliasing may occur when using the traditional discrete wavelet transform (DWT). Herein, a new denoising algorithm based on the dual-tree complex wavelet transform (DTCWT) is presented. Electrocardiogram (ECG) signals and heart sound signals are denoised based on the DTCWT. The results prove that the DTCWT is efficient. The signal-to-noise ratio (SNR) and the mean square error (MSE) are used to compare the denoising effect. Results of the paired samples t-test show that the new method can remove noise more thoroughly and better retain the boundary and texture of the signal.
authors.affiliation: Biomedical Engineering College of Chongqing University, Chongqing, 400030, China.
authors.forename: Fang
authors.initials: F
authors.lastname: Wang
authors.forename: Zhong
authors.initials: Z
authors.lastname: Ji
mesh_terms: D000465:Algorithms; D001145:Arrhythmias, Cardiac / Q000503:physiopathology; D003198:Computer Simulation; D016208:Databases, Factual; D004562:Electrocardiography / Q000379:methods*; D005583:Fourier Analysis; D006347:Heart Sounds; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D009991:Oscillometry; D015203:Reproducibility of Results; D012815:Signal Processing, Computer-Assisted; D059629:Signal-To-Noise Ratio; D058067:Wavelet Analysis
keywords: discrete wavelet transform; dual-tree complex wavelet transform; mean square error; paired samples t-test; signal-to-noise ratio