Notice of Violation of IEEE Publication PrinciplesECG Signal Interferences Removal Using Wavelet Based CSTD Technique

Notice of Violation of IEEE Publication Principles

"ECG Signal Interferences Removal Using Wavelet Based CSTD Technique,"
by R. Shantha Selva Kumari, A. Thilagamanimala, and V. Sadasivam
in the Proceedings of the International Conference on Computational Intelligence and Multimedia Applications, vol. 1, December 2007, pp. 530-534

After careful and considered review of the content and authorship of this paper by a duly constituted expert committee, this paper has been found to be in violation of IEEE\´s Publication Principles.

This paper contains significant portions of original text from the paper cited below. The original text was copied with insufficient attribution.

"Fast Wavelet Estimation of Weak Biosignals,"
by E. Causevic, R.E. Morley, M.V. Wickerhauser, and A.E. Jacquin,
in IEEE Transactions on Biomedical Engineering, vol.52, no.6, June 2005, pp. 1021-1032

Wavelet-based signal processing has become commonplace in the signal processing community over the past decade. One of the most important applications of wavelets is removal of noise from signals called denoising accomplished by thresholding wavelet coefficients in order to separate signal from noise. However, conventional denoising fails for signals with low signal-to-noise ratio (SNR). Synchronous linear averaging of a large number of acquired data frames is universally used to increase the SNR of weak biosignals. A novel wavelet-based estimator is presented for fast estimation of such weak ECG signals. This estimation algorithm provides a faster rate of convergence to the underlying signal than linear averaging by means of Cyclic Shift Tree Denoising (CSTD). This process is faster than other method and gives effective performance in SNR. In this paper CSTD technique is used for reducing Additive white Gaussian noise (or) random noise, EMG noise and Power line Interference. CSTD implies to recombine a set of N original data frames to create a large number of- new frames each of which is denoised using a variable threshold function. This algorithm uses the original (N) frames of data and produces (N*log 2 N) new frames. The new frame is derived by averaging pairs of adjacent original frames, followed by denoising each averaged frame. By this method improvement in SNR is 30.5 db. In preprocessing, the base line wander is removed using wavelet approximation of the ECG signal.