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Analysis of laser speckle contrast images variability using a novel empirical mode decomposition: comparison of results with laser Doppler flowmetry signals variability.

Authors
  • Humeau-Heurtier, Anne
  • Abraham, Pierre
  • Mahe, Guillaume
Type
Published Article
Journal
IEEE Transactions on Medical Imaging
Publisher
Institute of Electrical and Electronics Engineers
Publication Date
Feb 01, 2015
Volume
34
Issue
2
Pages
618–627
Identifiers
DOI: 10.1109/TMI.2014.2364079
PMID: 25347875
Source
Medline
License
Unknown

Abstract

Laser Doppler flowmetry (LDF) and laser speckle contrast imaging (LSCI) have emerged as noninvasive optical modalities to monitor microvascular blood flow. Many studies proposed to extract physiological information from LDF by analyzing signals variability. By opposition, such analyses for LSCI data have not been conducted yet. We propose to analyze LSCI variability using a novel data-driven method: the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN). CEEMDAN is suitable for nonlinear and nonstationary data and leads to intrinsic mode functions (IMFs). It is based on the ensemble empirical mode decomposition (EEMD) which relies on empirical mode decomposition (EMD). In our work the average frequencies of LSCI IMFs given by CEEMDAN are compared with the ones given by EMD and EEMD. Moreover, LDF signals acquired simultaneously to LSCI data are also processed with CEEMDAN, EMD and EEMD. We show that the average frequencies of IMFs given by CEEMDAN depend on the signal-to-noise ratio (SNR) used in the computation but, for a given SNR, the average frequencies found for LSCI are close to the ones obtained for LDF. By opposition, EEMD leads to IMFs with frequencies that do not vary much when the SNR level is higher than a threshold. The new CEEMDAN algorithm has the advantage of achieving a complete decomposition with no error in the reconstruction but our study suggests that further work is needed to gain knowledge in the adjustment of the added noise level. CEEMDAN, EMD and EEMD are data-driven methods that can provide a better knowledge of LSCI.

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