Analysis of Laser Speckle Contrast Images Variability Using a Novel Empirical Mode Decomposition: Comparison of Results With Laser Doppler Flowmetry Signals Variability

Humeau-Heurtier, Anne; Ábrahám, Pierre

doi:10.1109/tmi.2014.2364079

Cited by 34 publications

(20 citation statements)

References 60 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Most of the works on LSCI image processing are devoted to theoretical issues of processing raw speckle images [25], [26] or cerebral applications [27], [28], whereas the linear and nonlinear analysis of the main microvascular regulatory mechanisms developed for the LDF method could also benefit LSCI signal studies. This has been confirmed in [29] using an empirical mode decomposition analysis of LSCI and LDF signals variability.…”

Section: Introductionsupporting

confidence: 55%

Wavelet Analysis of the Temporal Dynamics of the Laser Speckle Contrast in Human Skin

Mizeva

Dremin

Potapova

et al. 2019

IEEE Trans. Biomed. Eng.

View full text Add to dashboard Cite

Objective: Spectral analysis of laser Doppler flowmetry (LDF) signals has been widely used in studies of physiological vascular function regulation. An alternative to LDF is the laser speckle contrast imaging method (LSCI), which is based on the same physical principle. In contrast to LDF, LSCI provides non-scanning full-field imaging of a relatively wide skin area and offers high spatial and temporal resolutions, which allows visualization of microvascular structure. This circumstance, together with a large number of works which had shown the effectiveness of temporal LSCI analysis, gave impetus to experimental studies of the relation between LDF and LSCI used to monitor the temporal dynamics of blood flow. Methods: Continuous wavelet transform was applied to construct a timefrequency representation of a signal. Results: Analysis of 10 minute LDF and LSCI output signals recorded simultaneously revealed rather high correlation between oscillating components. It was demonstrated for the first time that the spectral energy of oscillations in the 0.01-2 Hz frequency range of temporal LSCI recordings carries the same information as the conventional LDF recordings and hence it reflects the same physiological vascular tone regulation mechanisms. Conclusion: The approach proposed can be used to investigate speckle pattern dynamics by LSCI in both normal and pathological conditions. Significance: The results of research on the influence of spatial binning and averaging on the spectral characteristics of perfusion monitored by LSCI are of considerable interest for the development of LSCI systems optimized to evaluate temporal dynamics.

show abstract

Section: Introductionsupporting

confidence: 55%

Wavelet Analysis of the Temporal Dynamics of the Laser Speckle Contrast in Human Skin

Mizeva

Dremin

Potapova

et al. 2019

IEEE Trans. Biomed. Eng.

View full text Add to dashboard Cite

show abstract

“…However, EMD still has some drawbacks, such as mode mixing. Ensemble empirical mode decomposition (EEMD) was proposed to combine the white-noise-assisted system based on EMD to solve the problem of mode mixing [19,30]. If x(t) is the signal or time series to be decomposed, the EMD algorithm can be briefly summarized as follows:…”

Section: Ensemble Empirical Mode Decompositionmentioning

confidence: 99%

A Comparative Study of Groundwater Level Forecasting Using Data-Driven Models Based on Ensemble Empirical Mode Decomposition

Gong

Wang

et al. 2018

Water

View full text Add to dashboard Cite

Abstract:The reliable and accurate prediction of groundwater levels is important to improve water-use efficiency in the development and management of water resources. Three nonlinear time-series intelligence hybrid models were proposed to predict groundwater level fluctuations through a combination of ensemble empirical mode decomposition (EEMD) and data-driven models (i.e., artificial neural networks (ANN), support vector machines (SVM) and adaptive neuro fuzzy inference systems (ANFIS)), respectively. The prediction capability of EEMD-ANN, EEMD-SVM, and EEMD-ANFIS hybrid models was investigated using a monthly groundwater level time series collected from two observation wells near Lake Okeechobee in Florida. The statistical parameters correlation coefficient (R), normalized mean square error (NMSE), root mean square error (RMSE), Nash-Sutcliffe efficiency coefficient (NS), and Akaike information criteria (AIC) were used to assess the performance of the EEMD-ANN, EEMD-SVM and EEMD-ANFIS models. The results achieved from the EEMD-ANN, EEMD-SVM and EEMD-ANFIS models were compared with those from the ANN, SVM and ANFIS models. The three hybrid models (i.e., EEMD-ANN, EEMD-SVM, and EEMD-ANFIS) proved to be applicable to forecast the groundwater level fluctuations. The values of the statistical parameters indicated that the EEMD-ANFIS and EEMD-SVM models achieved better prediction results than the EEMD-ANN model. Meanwhile, the three models coupled with EEMD were found have better prediction results than the models that were not. The findings from this study indicate that the proposed nonlinear time-series intelligence hybrid models could improve the prediction capability in forecasting groundwater level fluctuations, and serve as useful and helpful guidelines for the management of sustainable water resources.

show abstract

“…1. However, as the projection is linear and the wavelets kernel is predefined, this kind of linear and nonadaptive signal decompositions is not effective for some applications that required nonlinear and adaptive signal processing [8][9][10].…”

Section: Introductionmentioning

confidence: 99%

“…Hence, the decomposition is adaptive. Moreover, the decomposition is nonlinear, so the EMD algorithm is useful for applications required nonlinear and adaptive signal processing [8][9][10]. However, as the sifting process is iterative and the IMFs are obtained only when the algorithm converges, no further iteration will be performed when the EMD algorithm is applied to these IMFs.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Nonlinear and adaptive undecimated hierarchical multiresolution analysis for real valued discrete time signals via empirical mode decomposition approach

Kuang

Yang

Ling

et al. 2015

Digital Signal Processing

View full text Add to dashboard Cite

Analysis of Laser Speckle Contrast Images Variability Using a Novel Empirical Mode Decomposition: Comparison of Results With Laser Doppler Flowmetry Signals Variability

Cited by 34 publications

References 60 publications

Wavelet Analysis of the Temporal Dynamics of the Laser Speckle Contrast in Human Skin

Wavelet Analysis of the Temporal Dynamics of the Laser Speckle Contrast in Human Skin

A Comparative Study of Groundwater Level Forecasting Using Data-Driven Models Based on Ensemble Empirical Mode Decomposition

Nonlinear and adaptive undecimated hierarchical multiresolution analysis for real valued discrete time signals via empirical mode decomposition approach

Contact Info

Product

Resources

About