S Boudaoud

Publications (2)2.28 Total impact

• Source

  Available from: Catherine K Marque

  Article: Combination of Canonical Correlation Analysis and Empirical Mode Decomposition Applied to Denoising the Labor Electrohysterogram

  M. Hassan, S. Boudaoud, J. Terrien, B. Karlsson, C. Marque
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  ABSTRACT: The electrohysterogram (EHG) is often corrupted by electronic and electromagnetic noise as well as movement artifacts, skeletal electromyogram, and ECGs from both mother and fetus. The interfering signals are sporadic and/or have spectra overlapping the spectra of the signals of interest rendering classical filtering ineffective. In the absence of efficient methods for denoising the monopolar EHG signal, bipolar methods are usually used. In this paper, we propose a novel combination of blind source separation using canonical correlation analysis (BSS_CCA) and empirical mode decomposition (EMD) methods to denoise monopolar EHG. We first extract the uterine bursts by using BSS_CCA then the biggest part of any residual noise is removed from the bursts by EMD. Our algorithm, called CCA_EMD, was compared with wavelet filtering and independent component analysis. We also compared CCA_EMD with the corresponding bipolar signals to demonstrate that the new method gives signals that have not been degraded by the new method. The proposed method successfully removed artifacts from the signal without altering the underlying uterine activity as observed by bipolar methods. The CCA_EMD algorithm performed considerably better than the comparison methods.
  IEEE Transactions on Biomedical Engineering 10/2011; · 2.28 Impact Factor
• Chapter: Evaluation of the MU Firing Strategies from Spectral Shape Analysis of sEMG Data

  M. Abi Hayla, S. Boudaoud, C. Marque
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  ABSTRACT: Studying the firing strategies of Motor Units (MU) from Surface Electromygram (sEMG) is of particular interest. The objective of this study is to explore the possible relationship between MU firing law shapes, given as a sEMG model input, with the shape of the evolution of spectral moments, obtained from the simulated EMG, in order to follow up the recruitment strategies of the MUs. For this purpose, a shape analysis is done by using the Distribution Function Method (DFM) to track similarities between two signals. The method is applied for analyzing the shape of the time evolution of spectral moment parameters computed on the PSD of the simulated EMG during isometric effort. We computed, using a recent sEMG model, 36 muscle configurations and generated sEMG sequences by MU time recruitment with an increasing firing rate followed by a decreasing part. Three patterns of firing variations are tested, 12 signals for each pattern, giving thus three classes of simulated sEMG. The (DFM) criterion is then used to quantify the shape dispersion among classes and between them, according to the firing pattern. In addition, the Integral Shape Averaging (ISA) approach has been used to get the class centroids. The obtained results are discussed with emphasis on the relevant ability for the spectral moments, coupled to shape analysis, to detect electrophysiological changes into muscles.
  12/2008: pages 326-329;