Tuyển tập báo cáo các nghiên cứu khoa học quốc tế ngành hóa học dành cho các bạn yêu hóa học tham khảo đề tài: Subband-Adaptive Shrinkage for Denoising of ECG Signals | Hindawi Publishing Corporation EURASIP Journal on Applied Signal Processing Volume 2006 Article ID 81236 Pages 1-9 DOI ASP 2006 81236 Subband-Adaptive Shrinkage for Denoising of ECG Signals S. Poornachandra1 and N. Kumaravel2 1 Department of Biomedical Engineering SSN College of Engineering Anna University Chennai 600025 India 2 Department of Electronics and Communication Engineering Anna University Chennai 600025 India Received 12 March 2005 Revised 8 September 2005 Accepted 28 September 2005 Recommended for Publication by Walter Kellermann This paper describes subband dependent adaptive shrinkage function that generalizes hard and soft shrinkages proposed by Donoho and Johnstone 1994 . The proposed new class of shrinkage function has continuous derivative which has been simulated and tested with normal and abnormal ECG signals with added standard Gaussian noise using MATLAB. The recovered signal is visually pleasant compared with other existing shrinkage functions. The implication of the proposed shrinkage function in denoising and data compression is discussed. Copyright 2006 Hindawi Publishing Corporation. All rights reserved. 1. INTRODUCTION Electrocardiogram ECG obtained by noninvasive technique is a harmless safe and quick method of cardiovascular diagnosis. The accuracy and content of information extracted from recording require proper characterization of waveform morphologies that needs better preservation of signals and higher attenuation of noise. Recently wavelet transform has proved to be a useful tool for nonstation-ary signal analysis. Wavelets provide flexible prototyping environment that comes with fast computational algorithms. A shrinkage method compares empirical wavelet coefficient with a threshold. The coefficient sets it to zero if its magnitude is less than threshold value 1 . The threshold acts as an oracle which distinguishes between significant and insignificant coefficients. Shrinkage of empirical wavelet coefficients works best
