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4 February 2010 Singularity detection by wavelet approach: application to electrocardiogram signal
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Proceedings Volume 7535, Wavelet Applications in Industrial Processing VII; 753506 (2010)
Event: IS&T/SPIE Electronic Imaging, 2010, San Jose, California, United States
In signal processing, the region of abrupt changes contains the most of the useful information about the nature of the signal. The region or the points where these changes occurred are often termed as singular point or singular region. The singularity is considered to be an important character of the signal, as it refers to the discontinuity and interruption present in the signal and the main purpose of the detection of such singular point is to identify the existence, location and size of those singularities. Electrocardiogram (ECG) signal is used to analyze the cardiovascular activity in the human body. However the presence of noise due to several reasons limits the doctor's decision and prevents accurate identification of different pathologies. In this work we attempt to analyze the ECG signal with energy based approach and some heuristic methods to segment and identify different signatures inside the signal. ECG signal has been initially denoised by empirical wavelet shrinkage approach based on Steins Unbiased Risk Estimate (SURE). At the second stage, the ECG signal has been analyzed by Mallat approach based on modulus maximas and Lipschitz exponent computation. The results from both approaches has been discussed and important aspects has been highlighted. In order to evaluate the algorithm, the analysis has been done on MIT-BIH Arrhythmia database; a set of ECG data records sampled at a rate of 360 Hz with 11 bit resolution over a 10mv range. The results have been examined and approved by medical doctors.
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Bushra Jalil, Ouadi Beya, Eric Fauvet, and Olivier Laligant "Singularity detection by wavelet approach: application to electrocardiogram signal", Proc. SPIE 7535, Wavelet Applications in Industrial Processing VII, 753506 (4 February 2010);

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