In the present work, a novel signal denoising technique for piecewise constant or linear signals is presented termed as "signal split." The proposed method separates the sharp edges or transitions from the noise elements by splitting the signal into different parts. Unlike many noise removal techniques, the method works only in the nonorthogonal domain. The new method utilizes Stein unbiased risk estimate (SURE) to split the signal, Lipschitz exponents to identify noise elements, and a polynomial fitting approach for the sub signal reconstruction. At the final stage, merging of all parts yield in the fully denoised signal at a very low computational cost. Statistical results are quite promising and performs better than the conventional shrinkage methods in the case of different types of noise, i.e., speckle, Poisson, and white Gaussian noise. The method has been compared with the state of the art SURE-linear expansion of thresholds denoising technique as well and performs equally well. The method has been extended to the multisplitting approach to identify small edges which are difficult to identify due to the mutual influence of their adjacent strong edges.
In this work, we attempt to propose a signal restoration technique from the noise corrupted signal. The main
diculty in most of the noise removal approaches is the extraction of singularities which are part of the signal
from noise elements. In order to over come this problem, the propose method measures the Lipschitz exponent of
the transitions to extract the noise elements. Unlike many noise removal techniques, the present method works
in the non orthogonal domain. These noise elements were identied from the decaying slope of modulus maxima
lines and is termed as Lipschitz exponents. The main contribution of the work is the reconstruction process.
By utilizing the property of Lipschitz exponents, it is possible to reconstruct the smooth signal by non linear
functioning. Statistical results are quite promising and performs better than conventional shrinkage methods
in the case of high variance noise. Furthermore, in order to extract noise elements the proposed method is not
limited with the selection of wavelet function for the addressed signal as well.
KEYWORDS: Electrocardiography, Denoising, Signal analysis, Signal detection, Heart, Interference (communication), Wavelet transforms, Fourier transforms, Signal processing, Signal analyzers
In this article, we present the method of empirical modal decomposition (EMD) applied to the electrocardiograms and
phonocardiograms signals analysis and denoising. The objective of this work is to detect automatically cardiac anomalies
of a patient. As these anomalies are localized in time, therefore the localization of all the events should be preserved
precisely. The methods based on the Fourier Transform (TFD) lose the localization property [13] and in the case of
Wavelet Transform (WT) which makes possible to overcome the problem of localization, but the interpretation remains
still difficult to characterize the signal precisely.
In this work we propose to apply the EMD (Empirical Modal Decomposition) which have very significant properties on
pseudo periodic signals. The second section describes the algorithm of EMD. In the third part we present the result
obtained on Phonocardiograms (PCG) and on Electrocardiograms (ECG) test signals. The analysis and the interpretation
of these signals are given in this same section. Finally, we introduce an adaptation of the EMD algorithm which seems to
be very efficient for denoising.
KEYWORDS: Signal detection, Wavelets, Electrocardiography, Signal analyzers, Electronic imaging, Current controlled current source, Signal processing, Pathology, Statistical analysis, Databases
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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