Advances in Electrocardiogram Signal Processing and Analysis
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Editorial Advances in Electrocardiogram Signal Processing and Analysis William Sandham,1 David Hamilton,2 Pablo Laguna,3 and Maurice Cohen4 1 Scotsig,
Glasgow G12 9PF, UK Limited, Edinburgh EH3 8EG, UK 3 Department of Electronic Engineering and Communications, Zaragoza University, 50018 Zaragoza, Spain 4 UCSF Fresno Center for Medical Education, School of Medicine, University of California, San Francisco, Fresno, CA 93701-2302, USA 2 Ateeda
Received 3 April 2007; Accepted 3 April 2007 Copyright © 2007 William Sandham et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Since itsinvention by the Dutchman Willem Einthoven (1860–1927) during the late 19th and early 20th centuries, when it was little more than a scientific curiosity, the electrocardiogram (ECG) has developed into one of the most important and widely used quantitative diagnostic tools in medicine. It is essential for the identification of disorders of the cardiac rhythm, extremely useful for the diagnosis and management of heart abnormalities such as myocardial infarction (heart attack), and it offers helpful clues to the presence of generalized disorders that affect the rest of the body, such as electrolyte disturbances and drug intoxication. Recording and analysis of the ECG now involve a considerable amount of signal processing; for S/N enhancement, beat detection and delineation, automated classification, compression, hidden information extraction, and dynamic modeling. These involve a whole variety of innovative signal processing methods, including adaptive techniques, time-frequency and time-scale procedures, artificial neural networks and fuzzy logic, higher-order statistics and nonlinear schemes, fractals, hierarchical trees, Bayesian approaches, and parametric models, amongst others. This special issue reviews the current status of ECG signal processing and analysis, with particular regard to recent innovations. It reports major achievements by academic and commercial research institutions and individuals, and provides an insight into future developments within this exciting and challenging area. It is perhaps appropriate that a special issue of EURASIP JASP be devoted to ECG signal processing and analysis, since the ECG is now celebrating its centennial (Dijk and Van Loon [1]). The first paper, “Multiadaptive bionic wavelet transform: application to ECG denoising and baseline wandering reduction,” by O. Sayadi and M. B. Shamsollahi, describes a new
modified wavelet transform that can be used to remove a wide range of noise from an ECG signal. Signal decomposition is obtained using the bionic wavelet transform, adaptively determining both the center frequency of each scale, together with the T-function. A threshold rule is then applied. The method was tested with both real and simulated ECG signals. Results demonstrate a significantly better noise reduction compared with stand
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