Myopotential denoising of ECG signals using wavelet thresholding methods

Neural Networks

Volumn 14, Issue 8, 2001, Pages 1129-1137

  Cherkassky, Vladimir ^a   Kilts, Steven ^a  

^a University of Minnesota   (United States)

Author keywords

Complexity control; ECG denoising; Model selection; Myopotential noise; Signal denoising; VC theory; Wavelet thresholding

Indexed keywords

ROBUSTNESS (CONTROL SYSTEMS); THRESHOLD LOGIC; WAVELET TRANSFORMS;

MYOPOTENTIAL DENOISING;

ELECTROCARDIOGRAPHY;

ACCURACY; ANALYTIC METHOD; ARTICLE; ELECTROCARDIOGRAM; IMAGE QUALITY; LEARNING; MUSCLE POTENTIAL; PRIORITY JOURNAL; SIGNAL PROCESSING; THEORY; WAVEFORM;

EID: 0034842313     PISSN: 08936080     EISSN: None     Source Type: Journal    
DOI: 10.1016/S0893-6080(01)00041-7     Document Type: Article

References (11)

1. Wavelet analysis
2. Nonlinear wavelet image processing: Variational problems, compression and noise removal through wavelet shrinkage
3. Model complexity control for regression using VC generalization bounds
4. Model selection for wavelet-based signal estimation
5. Signal estimation and denoising using VC-theory
6. Data compression and harmonic analysis
7. Denoising by soft thresholding
8. Ideal denoising in an orthonormal basis chosen from a library of bases