A framework based on the information domain to measure coupling changes in electrophysiological signals

Objectives.In this paper, the features of physiological signals of healthy dataset are extracted using the linear and non-linear techniques, and a comparison has been made on healthy young and old subjects to study the aging and gender-related changes in the contribution of Heart Rate (HR), Blood Pressure (BP), and Respiration (RESP).Methods. To quantify the coupling changes in cardiovascular, cardiorespiratory, and vasculorespiratory complexity, an information domain approach based on compensated transfer entropy (cTE) is proposed.Result. The results show that there is a substantial decrease in the flow of information from BP tro the time interval between successive R-peaks (RR) and from RR to BP. There is also a significant decrease in the flow of information from RESP to BP and RESP to RR but there is no significant change in the information flow from BP to RESP and RR to RESP.Conclusion. We have done linear and non-linear analysis on the healthy datasets of young and old subjects. As already existed techniques lacks in studying complex behaviours of electrophysiological signals so to overcome these limitations, we have proposed compensated transfer entropy (cTE). We conducted an investigation to determine the degree to which recordings of RESP, BP, and HR can be utilized to predict changes in the other parameters. Specifically, the proposed analysis examined the relationship between these variables and assessed their consistency across different age groups and genders. By analyzing the data, we aimed to gain insights into the interdependencies and predictive potential of these physiological measures in relation to each other.

Development of Postural Stability Index to Distinguish Different Stability States

A key factor for fall prevention involves understanding the pathophysiology of stability. This study proposes the postural stability index (PSI), which is a novel measure to quantify different stability states on healthy subjects. The results of the x-, y-, and z-axes of the acceleration signals were analyzed from 10 healthy young adults and 10 healthy older adults under three conditions as follows: Normal walking, walking with obstacles, and fall-like motions. The ensemble empirical mode decomposition (EEMD) was used to reconstruct the acceleration signal data. Wearable accelerometers were located on the ankles and knees of the subjects. The PSI indicated a decreasing trend of its values from normal walking to the fall-like motions. Free-walking data were used to determine the stability based on the PSI. The segmented free-walking data indicated changes in the stability states that suggested that the PSI is potentially helpful in quantifying gait stability.

Bi-dimensional multiscale entropy: Relation with discrete Fourier transform and biomedical application

The multiscale entropy (MSE_1D) measure is now widely used to quantify the complexity of time series. The development of complexity measures for images is also a long-standing goal. Recently, the bi-dimensional version of MSE_1D has been proposed (MSE_2D) to analyze images. The interpretation of MSE_2D curves and the applications to real data are still emergent. Because the coarse-graining step in the MSE_2D computation changes the frequency content of the image, we hypothesized a possible dependence between MSE_2D and the discrete Fourier transform (DFT). To analyze this dependence, synthetic as well as biomedical images are analyzed. Our results reveal that i) the profile of MSE_2D is sensitive to both the amplitude and phase of the DFT; ii) MSE_2D could find applications in the biomedical field. This work brings valuable information for MSE_2D interpretation and opens possibilities to study images from an entropy point of view through spatial scales.

Centered and Averaged Fuzzy Entropy to Improve Fuzzy Entropy Precision

Several entropy measures are now widely used to analyze real-world time series. Among them, we can cite approximate entropy, sample entropy and fuzzy entropy (FuzzyEn), the latter one being probably the most efficient among the three. However, FuzzyEn precision depends on the number of samples in the data under study. The longer the signal, the better it is. Nevertheless, long signals are often difficult to obtain in real applications. This is why we herein propose a new FuzzyEn that presents better precision than the standard FuzzyEn. This is performed by increasing the number of samples used in the computation of the entropy measure, without changing the length of the time series. Thus, for the comparisons of the patterns, the mean value is no longer a constraint. Moreover, translated patterns are not the only ones considered: reflected, inversed, and glide-reflected patterns are also taken into account. The new measure (so-called centered and averaged FuzzyEn) is applied to synthetic and biomedical signals. The results show that the centered and averaged FuzzyEn leads to more precise results than the standard FuzzyEn: the relative percentile range is reduced compared to the standard sample entropy and fuzzy entropy measures. The centered and averaged FuzzyEn could now be used in other applications to compare its performances to those of other already-existing entropy measures.

Aging effect on microcirculation: A multiscale entropy approach on laser speckle contrast images

PURPOSE

It has long been known that age plays a crucial role in the deterioration of microvessels. The assessment of such deteriorations can be achieved by monitoring microvascular blood flow. Laser speckle contrast imaging (LSCI) is a powerful optical imaging tool that provides two-dimensional information on microvascular blood flow. The technique has recently been commercialized, and hence, few works discuss the postacquisition processing of laser speckle contrast images recorded in vivo. By applying entropy-based complexity measures to LSCI time series, we present herein the first attempt to study the effect of aging on microcirculation by measuring the complexity of microvascular signals over multiple time scales.

METHODS

Forearm skin microvascular blood flow was studied with LSCI in 18 healthy subjects. The subjects were subdivided into two age groups: younger (20-30 years old, n = 9) and older (50-68 years old, n = 9). To estimate age-dependent changes in microvascular blood flow, we applied three entropy-based complexity algorithms to LSCI time series.

RESULTS

The application of entropy-based complexity algorithms to LSCI time series can differentiate younger from older groups: the data fluctuations in the younger group have a significantly higher complexity than those obtained from the older group.

CONCLUSIONS

The effect of aging on microcirculation can be estimated by using entropy-based complexity algorithms to LSCI time series.

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

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.

Entropy analysis reveals a simple linear relation between laser speckle and blood flow

Dynamic laser speckles contain motion information of scattering particles which can be estimated by laser speckle contrast analysis (LASCA). In this work, an entropy-based method was proposed to provide a more robust estimation of motion speed. An in vitro flow simulation experiment confirmed a simple linear relation between entropy, exposure time, and speed. A multimodality optical imaging setup is developed to validate the advantages of the entropy method based on laser speckle imaging, green light imaging, and fluorescence imaging. The entropy method overcomes traditional LASCA with less noisy interference, and extracts more visible and detailed vasculatures in vivo. Furthermore, the entropy method provides a more accurate estimation and a stable pattern of blood flow activations in the rat's somatosensory area under multitrial hand paw stimulations.

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.

Detecting physiological systems with laser speckle perfusion imaging of the renal cortex

Laser speckle perfusion imaging (LSPI) has become an increasingly popular technique for monitoring vascular perfusion over a tissue surface. However, few studies have utilized the full range of spatial and temporal information generated by LSPI to monitor spatial properties of physiologically relevant dynamics. In this study, we extend the use of LSPI to analyze renal perfusion dynamics over a spatial surface of ~5 × 7 mm of renal cortex. We identify frequencies related to five physiological systems that induce temporal changes in renal vascular perfusion (cardiac flow pulse, respiratory-induced oscillations, baroreflex components, the myogenic response, and tubuloglomerular feedback) across the imaged surface and compare the results with those obtained from renal blood flow measurements. We find that dynamics supplied from global sources (cardiac, respiration, and baroreflex) present with the same frequency at all locations across the imaged surface, but the local renal autoregulation dynamics can be heterogeneous in their distribution across the surface. Moreover, transfer function analysis with forced blood pressure as the input yields the same information with laser speckle imaging or renal blood flow as the output during control, intrarenal infusion of N(ω)-nitro-L-arginine methyl ester to enhance renal autoregulation, and intrarenal infusion of the rho-kinase inhibitor Y-27632 to inhibit vasomotion. We conclude that LSPI measurements can be used to analyze local as well as global renal perfusion dynamics and to study the properties of physiological systems across the renal cortex.