Laser Doppler et microcirculation

à Paris (France) le 23/09/2009Journée du club "Société Francophone pour l'Informatique et le Monitorage en Anesthésie Réanimation" (SFIMAR) du congrès de la Société Française d'Anesthésie Réanimation (SFAR

Texture feature extraction methods: A survey

Texture analysis is used in a very broad range of fields and applications, from texture classification (e.g., for remote sensing) to segmentation (e.g., in biomedical imaging), passing through image synthesis or pattern recognition (e.g., for image inpainting). For each of these image processing procedures, first, it is necessary to extract—from raw images—meaningful features that describe the texture properties. Various feature extraction methods have been proposed in the last decades. Each of them has its advantages and limitations: performances of some of them are not modified by translation, rotation, affine, and perspective transform; others have a low computational complexity; others, again, are easy to implement; and so on. This paper provides a comprehensive survey of the texture feature extraction methods. The latter are categorized into seven classes: statistical approaches, structural approaches, transform-based approaches, model-based approaches, graph-based approaches, learning-based approaches, and entropy-based approaches. For each method in these seven classes, we present the concept, the advantages, and the drawbacks and give examples of application. This survey allows us to identify two classes of methods that, particularly, deserve attention in the future, as their performances seem interesting, but their thorough study is not performed yet

Refined composite multiscale permutation entropy to overcome multiscale permutation entropy length dependence

Multiscale permutation entropy (MPE) has recently been proposed to evaluate complexity of time series. MPE has numerous advantages over other multiscale complexity measures, such as its simplicity, robustness to noise and its low computational cost. However, MPE may loose statistical reliability as the scale factor increases, because the coarse-graining procedure used in the MPE algorithm reduces the length of the time series as the scale factor grows. To overcome this drawback, we introduce the refined composite MPE (RCMPE). Through applications on both synthetic and real data, we show that RCMPE is much less dependent on the signal length than MPE. In this sense, RCMPE is more reliable than MPE. RCMPE could therefore replace MPE for short times series or at large scale factors

Modeling and interpretation of the bioelectrical impedance signal for the determination of the local arterial stiffness

Purpose: Stiffness of the large arteries (e.g., aorta) plays an important role in the pathogenesis of cardiovascular diseases. To date, the reference method for the determination of regional arterial stiffness is the measurement of the carotid-femoral pulse wave velocity (PWV) by tonometric techniques. However, this method suffers from several drawbacks and it remains limited in clinical routine.Methods: In the present study, the authors propose a new method based on the analysis of bioelectrical impedance (BI) signals for the determination of the local arterial stiffness. They show, from a theoretical model, a novel interpretation of the BI signals and they establish the relationship between the variations in the BI signal and the kinetic energy of the blood flow in large arteries. From this model, BI signals are simulated in the thigh and compared to experimental BI data. Finally, from the model, they propose a new index ( Ira ) related to the properties of the large artery for the determination of the local arterial stiffness. Results: The results show a good correlation between the simulated and the experimental BI signals. The same variations for both of them with different characteristics for rigid and elasticarteries can be observed. The measurement of the Ira index on 20 subjects at rest (mean age of 44 ± 16 yr ) for the determination of the local aortic stiffness presents a significant correlation with the PWV reference method ( R 2 = 0.77 ; P < 0.0001 with the Spearman correlation coefficient and Ira = 4.25 * PWV + 23.54 ). Conclusions: All the results suggest that the theoretical model and the new index could give a reliable estimate of local arterial stiffness

Instrumentation, modélisation, et traitement des signaux biomédicaux reflétant la microcirculation sanguine : contribution à la technique de fluxmétrie laser Doppler

National audienc

Refined scale-dependent permutation entropy to analyze systems complexity

Multiscale entropy (MSE) has become a prevailing method to quantify the complexity of systems. Unfortunately, MSE has a temporal complexity in O(N2)O(N2), which is unrealistic for long time series. Moreover, MSE relies on the sample entropy computation which is length-dependent and which leads to large variance and possible undefined entropy values for short time series. Here, we propose and introduce a new multiscale complexity measure, the refined scale-dependent permutation entropy (RSDPE). Through the processing of different kinds of synthetic data and real signals, we show that RSDPE has a behavior close to the one of MSE. Furthermore, RSDPE has a temporal complexity in O(N)O(N). Finally, RSDPE has the advantage of being much less length-dependent than MSE. From all this, we conclude that RSDPE over-performs MSE in terms of computational cost and computational accuracy

Linguistic analysis of laser speckle contrast images recorded at rest and during biological zero: comparison with laser Doppler flowmetry data

Laser speckle contrast imaging (LSCI) is a newly commercialized imaging modality to monitor microvascular blood flow. Contrary to the well-known laser Doppler flowmetry (LDF), LSCI has the advantage of giving a full-field image of surface blood flow using simple instrumentation. However, laser speckle contrast images are not fully understood yet and their link with LDF signals still has to be studied. To quantify the similarity between LSCI and LDF symbolic sequences, we propose to use, for the first time, the index adapted from linguistic analysis and information theory proposed by Yang For this purpose, LSCI and LDF data were recorded simultaneously on the forearm of healthy subjects, at rest and during a vascular occlusion (biological zero). We show that there are different dynamical patterns for LSCI and LDF data, and the distances between these patterns differ through the space scales explored. Moreover, our results suggest that these different dynamical patterns could be linked to blood flow. The quantitative metric used herein therefore provides new information on LSCI and brings knowledge on links between LSCI and LDF