ECG Signal Denoising by Morphological Top-Hat Transform

The electrocardiogram ECG signal plays an important role in the primary diagnosis, prognosis and survival analysis of heart diseases. The ECG signal contains an important amount of information that can be exploited in different manners. However, during its acquisition it is often contaminated with different sources of noise making difficult its interpretation. In this paper, a new approach based on Morphological Top-Hat Transform (MTHT) is developed in order to suppress noises from the ECG signals. The morphological operators (dilation, erosion, opening, closing) constitute the fundamental stage of Top-Hat transform. Method presented in this paper is compared with the Visu Shrink, Sure Shrink, and Bayes Shrink methods. The experimental results indicated that the proposed methods in this work were better than the compared methods in terms of retaining the geometrical characteristics of the ECG signal, SNR. Due to its simplicity and its fast implementation, the method can easily be used in clinical medicine.

[1]  Brij N. Singh,et al.  Optimal selection of wavelet basis function applied to ECG signal denoising , 2006, Digit. Signal Process..

[2]  D. L. Donoho,et al.  Ideal spacial adaptation via wavelet shrinkage , 1994 .

[3]  Lei Zhang,et al.  Canny edge detection enhancement by scale multiplication , 2005, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[4]  Lei Zhang,et al.  Edge detection by scale multiplication in wavelet domain , 2002, Pattern Recognit. Lett..

[5]  P Caminal,et al.  Automatic detection of wave boundaries in multilead ECG signals: validation with the CSE database. , 1994, Computers and biomedical research, an international journal.

[6]  I. Johnstone,et al.  Adapting to Unknown Smoothness via Wavelet Shrinkage , 1995 .

[7]  David L. Donoho,et al.  De-noising by soft-thresholding , 1995, IEEE Trans. Inf. Theory.

[8]  Martin Vetterli,et al.  Adaptive wavelet thresholding for image denoising and compression , 2000, IEEE Trans. Image Process..

[9]  Olivier Y. De Vel.  R-Wave Detection in the Presence of Muscle Artifacts , 1984, IEEE Transactions on Biomedical Engineering.

[10]  Petros Maragos,et al.  Morphological filters-Part I: Their set-theoretic analysis and relations to linear shift-invariant filters , 1987, IEEE Trans. Acoust. Speech Signal Process..