Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
4977711 | Signal Processing | 2017 | 12 Pages |
Abstract
The denoising of normal and abnormal electrocardiogram (ECG) signals in different stationary and non-stationary noise levels is studied as case study. While most ECG denoising techniques benefit from the pseudo-periodicity of the ECG, the developed technique is merely based on the smoothness assumption, which makes it a powerful method for both normal and abnormal ECG. The performance of the method is assessed by Monte-Carlo simulations over three standard normal and abnormal ECG databases of different sampling rates, in comparison with bandpass filtering, wavelet denoising with various parameters, and Savitzky-Golay filters using Stein's unbiased risk estimate shrinkage scheme.
Related Topics
Physical Sciences and Engineering
Computer Science
Signal Processing
Authors
Reza Sameni,