Search

In the domain of computer vision, entropy-defined as a measure of irregularity-has been proposed as an effective method for analyzing the texture of images. Several studies have shown that, with specific parameter tuning, entropy-based approaches achieve high accuracy in terms of classification results for texture images, when associated with machine learning classifiers. However, few entropy measures have been extended to studying color images. Moreover, the literature is missing comparative analyses of entropy-based and modern deep learning-based classification methods for RGB color images. In order to address this matter, we first propose a new entropy-based measure for RGB images based on a multivariate approach. This multivariate approach is a bi-dimensional extension of the methods that have been successfully applied to multivariate signals (unidimensional data). Then, we compare the classification results of this new approach with those obtained from several deep learning methods. The entropy-based method for RGB image classification that we propose leads to promising results. In future studies, the measure could be extended to study other color spaces as well.

Atrial fibrillation (AF) is the most common supraventricular cardiac arrhythmia, resulting in high mortality rates among affected patients. AF occurs as episodes coming from irregular excitations of the ventricles that affect the functionality of the heart and can increase the risk of stroke and heart attack. Early and automatic prediction, detection, and classification of AF are important steps for effective treatment. For this reason, it is the subject of intensive research in both medicine and engineering fields. The latter research focuses on three axes: prediction, classification, and detection. Knowing that AF is often asymptomatic and that its episodes are often very short, its automatic early detection is a very complicated but clinically important task to improve AF treatment and reduce the risks for the patients. This article is a review of publications from the past decade, focusing on AF episode prediction, detection, and classification using wavelets and artificial intelligence (AI). Forty-five articles were selected of which five are about AF in general, four articles compare accuracy, recall and precision between Fourier transform (FT) and wavelets transform (WT), and thirty-six are about detection, classification, and prediction of AF with WT: 15 are based on deep learning (DL) and 21 on conventional machine learning (ML). Of the thirty-six studies, thirty were published after 2015, confirming that this particular research area is very important and has great potential for future research., (Copyright © 2022 Elsevier Ltd. All rights reserved.)

Radiologists, and doctors in general, need relevant information for the quantification and characterization of pulmonary structures damaged by severe diseases, such as the Coronavirus disease 2019 (COVID-19). Texture-based analysis in scope of other pulmonary diseases has been used to screen, monitor, and provide valuable information for several kinds of diagnoses. To differentiate COVID-19 patients from healthy subjects and patients with other pulmonary diseases is crucial. Our goal is to quantify lung modifications in two pulmonary pathologies: COVID-19 and idiopathic pulmonary fibrosis (IPF). For this purpose, we propose the use of a three-dimensional multiscale fuzzy entropy (MFE3D) algorithm. The three groups tested (COVID-19 patients, IPF, and healthy subjects) were found to be statistically different for 9 scale factors ( p < 0.01 ). A complexity index (CI) based on the sum of entropy values is used to classify healthy subjects and COVID-19 patients showing an accuracy of 89.6 % , a sensitivity of 96.1 % , and a specificity of 76.9 % . Moreover, 4 different machine-learning models were also used to classify the same COVID-19 dataset for comparison purposes., (© 2021 Elsevier Ltd. All rights reserved.)

Studying the impact of age is important to understand the phenomenon of aging and the disorders that are associated with it. In this work, we analyze age-related alterations on the capacities to navigate on a bike. For this purpose, we use CycléoONE, a bike simulator, and entropy measures. We thus record navigation data (handlebar angle and speed) during the ride. They are processed with two cross-distribution entropy methods (time-shift multiscale cross-distribution entropy and multiscale cross-distribution entropy). We also analyze the time series with a detrended cross-correlation analysis to determine which method can best underline age-related alterations. Our results show that methods based on cross-distribution entropy may be efficient to stress the decrease in navigation capacities with age. The results are very encouraging for our future goal of adding medical benefits to a leisure equipment. They also show the value of using virtual reality to study the impact of age. Graphical Abstract This study deals with the use of the signal processing methods (multiscale cross-entropy and multiscale cross-correlation) applied on naviagtion data, acquired with a bike simulator, to study the impact of age on two populations (young healthy subjects and older adults with loss of autonomy).

We present herein a new approach - the so-called time-shift multiscale cross-distribution entropy (TSMCDE) - to quantify the complexity between two sequences. By analyzing biomedical data, we reveal that TSMCDE over-performs other cross-entropy measures. Thus, TSMCDE, multiscale cross-sample entropy (MCSE), multiscale cross-distribution entropy (MCDE), and time-shift multiscale cross-sample entropy (TSMCSE) were applied to handlebar angle and speed time series recorded from a bike simulator. Twenty-four subjects divided into two groups (12 subjects each) participated to the study. The first group corresponds to young healthy subjects. The second group corresponds to older adults with loss of autonomy. Our results show that a link may exist between complexity and the age and physical state of a population. Moreover, TSMCDE leads to a better differentiation of the two groups than MCSE, MCDE, and TSMCSE. TSMCDE should now be tested on other types of data and on larger datasets to prove its usefulness and its efficiency.

Since the 2010's, entropy measures have been extended from the 1D to the 2D case to deal with images and are forming potent bidimensional irregularity measures. In our work, we study a new 2D entropy measure - the so-called bidimensional fuzzy entropy (FuzEn 2D ) - that out-performs existing bidimensional entropy measures. We first assess its sensitivity to parameters, then analyze its behavior upon rotation and translation, and finally show its multiscale application in the biomedical field (dermoscopic images). To validate the output of the newly introduced FuzEn 2D and its multiscale extension, a set of synthetic images based on several concepts in image processing (including power of noise and degree of randomness) and texture datasets are used. The results for synthetic images illustrate that FuzEn 2D has low sensitivity to the chosen parameters and it is rotation and translation invariant. Moreover, it outperforms the already existing bidimensional entropy measures. Finally, we evaluate dermoscopic melanoma (malignant lesion) and melanocytic nevi (benign lesion) images and the results are found to be interesting for a potential diagnostic application.

Functional Connectivity (FC) is a powerful tool to investigate brain networks both in rest and while performing tasks. Functional magnetic resonance imaging (fMRI) gave good spatial estimation of FC but lacked the temporal resolution. Electroencephalography (EEG) allows estimating FC with good temporal resolution. In this study we introduce a new method based on Mutual Information and Multivariate Improved Weighted Multi-scale Permutation Entropy to estimate FC of brain using EEG. We applied this method on resting-state EEG signals from healthy children. Using network measures of nodes and Wilcoxon signed-rank test, we identified the most important nodes in the estimated networks. Comparing the localization of those outstanding nodes with the regions involved in resting-state networks (RSNs) estimated from fMRI showed that our proposal is efficient in the identification of nodes belonging to RSNs and could be used as a general estimator for FC without having to band-pass the signals into frequency bands.

Background: Forearm cutaneous blood flux (CBF) measurement with post-occlusive reactive hyperemia (PORH) is uncomfortable and may not be devoid of risks. We aimed to investigate post-compression reactive hyperemia (PCRH) with a custom-made indenter that was designed to be easily used routinely by inexperienced observers., Methods: Medical students evaluated PCRH with 1- to 4-min pressure applications of 16 to 34 kPa and PORH with 3-min forearm cuff occlusion using laser speckle contrast imaging in 15 healthy volunteers. Participants were asked to quantify their discomfort with a visual analogue scale (VAS) of 10 cm. Total ischemia (ISCH) was quantified by the product of CBF during ischemia and ischemia duration (min). We subtracted the CBF changes in the skin from a reference ipsilateral (PCRH) or contralateral (PORH) non-stimulated area., Results: The average VAS was 1.0 for PCRH vs. 6.0 for PORH (p < 0.001). A strong linear relationship between ISCH and peak PCRH (r 2  = 0.915, p < 0.001) was noted. Peak PORH values (63.9 laser perfusion units (LPU)) were significantly lower than all values of the 3-min PCRH (72.6 LPU), including the one obtained with 16 kPa., Conclusion: Inexperienced observers could test microvascular reactivity with PCRH without inducing the discomfort that is typically experienced with PORH. Further, PCRH elicits a higher peak response to ischemia compared with PORH. This extremely simple method could influence a broad spectrum of routine cutaneous microcirculation investigations, especially when a painful approach is particularly inadequate or if the patient is fragile., Clinical Trial Registration: NCT02861924., (Copyright © 2018 Elsevier Inc. All rights reserved.)

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., (Copyright © 2018 Elsevier Ltd. All rights reserved.)

Background and Aims: The first cause of low-dose acetylsalicylic-acid (ASA) inefficacy is poor adherence to treatment. No non-invasive technique is available to assess ASA intake. Current-induced vasodilation (CIV) was found abolished in healthy volunteers after low-dose ASA intake. We tested clinical characteristics, treatments, and comorbid conditions influencing CIV amplitude in vascular patients., Methods: CIV was tested in 400 patients (277 males and 123 females, aged 65.4 ± 13.4 years). We focused on clinical characteristics, treatments, and comorbid conditions as covariates of CIV amplitude. We studied the CIV amplitude to covariate relationships with multivariate linear regression and receiver operating characteristics (ROC)., Results: The multivariate linear model determined that ASA intake within the last 48 h and the interaction between ASA intake and body mass index (BMI) were the sole covariates associated with CIV amplitude. For the whole population, the area under the ROC curve (AUC) for CIV to predict ASA intake was 0.853 [95% confidence interval (CI): 0.814-0.892]. Considering separately the areas observed for non-obese (BMI ≤30, n = 303) and obese (BMI>30, n = 93) patients, the AUC [95% CI] was 0.873 [0.832-0.915] and 0.776 [0.675-0.878], respectively (p = 0.083)., Conclusions: ASA is the only drug that affects the amplitude of CIV response observed after galvanic current application to the skin of vascular patients. CIV depends on BMI but not age or gender. As such, CIV appears to be a potential objective marker of ASA intake and could facilitate future non-invasive assessments of adherence to ASA treatment., (Copyright © 2018 Elsevier B.V. All rights reserved.)

The number of patients suffering from peripheral arterial occlusive disease (PAOD) is growing worldwide. PAOD corresponds to an impairment of perfusion in the distal portion of some arteries that can generate critical limb ischemia. For some patients, the pathology leads to the necessity of amputation. PAOD may be diagnosed and studied with transcutaneous oxygen (tcpO2) measurements. However, the tcpO2 technique is a contact technique: probes have to be positioned on the limb, which may be impossible in case of ulcers and can lead to infection risks. We therefore herein propose to study if the laser speckle contrast imaging (LSCI) technique, an optical contactless technique, could be of interest in studying limb ischemia. For this purpose, tcpO2 and LSCI data were recorded in 48 persons suffering or not from PAOD (different levels of criticality). The LSCI data were then processed with the recently introduced bi-dimensional variational mode decomposition (2D-VMD). 2D-VMD leads to band-limited intrinsic mode functions (LSCI 2D-BLIMFs in our case). From these LSCI 2D-BLIMFs, we propose a new index. Our results show that this index and the tcpO2 values have a Pearson's r correlation coefficient of -0.401. Moreover, the Bland-Altman graph shows that the LSCI 2D-BLIMFs-based index and the tcpO2 values are consistent. LSCI might therefore be of interest for a contactless study of limb ischemia., (Copyright © 2017 Elsevier Ltd. All rights reserved.)

Purpose: Laser speckle contrast imaging (LSCI) continues to gain an increased interest in clinical and research studies to monitor microvascular perfusion. Due to its high spatial and temporal resolutions, LSCI may lead to a large amount of data. The analysis of such data, as well as the determination of the regions where the perfusion varies, can become a lengthy and tedious task. We propose here to analyze if a view-based temporal template method, the motion history image (MHI) algorithm, may be of use in detecting the perfusion variations locations., Methods: LSCI data recorded during three different kinds of perfusion variations are considered: (i) cerebral blood flow during spreading depolarization (SD) in a mouse; (ii) cerebral blood flow during SD in a rat; (iii) cerebral blood flow during cardiac arrest in a rat. Each of these recordings was processed with MHI., Results: We show that, for the three pathophysiological situations, MHI identifies the area in which perfusion evolves with time. The results are more easily obtained compared with a visual inspection of all of the frames constituting the recordings. MHI also has the advantage of relying on a rather simple algorithm., Conclusions: MHI can be tested in clinical and research studies to aid the user in perfusion analyses., (Copyright © 2017 Elsevier Inc. All rights reserved.)

Background and Objective: Pseudoxanthoma elasticum (PXE) is an inherited and systemic metabolic disorder that affects the skin, leading among other things to a peau d'orange appearance. Unfortunately, PXE is still poorly understood and there is no existing therapy to treat the disease. Because the skin is the first organ to be affected in PXE, we propose herein a study of skin microvascular perfusion. By means of this analysis, our goal is to increase knowledge of PXE., Methods: For this purpose, microvascular data from patients suffering from PXE and from healthy control subjects were recorded using the laser speckle contrast imaging (LSCI) modality. These data were processed using the recent 2D version of the unconstrained optimization approach to empirical mode decomposition (UOA-EMD). Our work therefore corresponds to the first time this algorithm has been applied to biomedical data., Results: Our study shows that the 2D-UOA-EMD is able to reveal spatial patterns on local textures of LSCI data. Moreover, these spatial patterns differ between PXE patients and control subjects. Quantification measure of these spatial patterns reveals statistical significant differences between PXE and control subjects, in the neck (p=0.0004) and in the back (p=0.0052)., Conclusions: For the first time, alterations in microvascular perfusion in PXE patients have been revealed. Our findings open new avenues for our understanding of pathophysiologic skin changes in PXE., (Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.)

The diagnosis of pathologies from signal processing approaches has shown to be of importance. This can provide noninvasive information at the earliest stage. In this work, the problem of categorising - in a quantifiable manner - information content of microvascular blood flow signals recorded in healthy participants and patients with varicose veins is addressed. For this purpose, laser Doppler flowmetry (LDF) signals - that reflect microvascular blood flow - recorded both at rest and after acetylcholine (ACh) stimulation (an endothelial-dependent vasodilator) are analyzed. Each signal is processed with the empirical mode decomposition (EMD) to obtain its intrinsic mode functions (IMFs). An entropy measure of each IMFs is then computed. The results show that IMFs of LDF signals have different complexity for different physiologic/pathological states. This is true both at rest and after ACh stimulation. Thus, the proposed framework (EMD + entropy computation) may be used to gain a noninvasive understanding of LDF signals in patients with microvascular dysfunctions., (Copyright © 2015 IPEM. Published by Elsevier Ltd. All rights reserved.)

Laser speckle contrast imaging (LSCI) is a full-field optical technique to monitor microvascular blood flow with high spatial and temporal resolutions. It is used in many medical fields such as dermatology, vascular medicine, or neurosciences. However, LSCI leads to a large amount of data: image sampling frequency is often of several Hz and recordings usually last several minutes. Therefore, clinicians often perform regions of interest in which a spatial averaging of blood flow is performed and the result is followed with time. Unfortunately, this leads to a poor spatial resolution for the analyzed data. At the same time, a higher spatial resolution for the perfusion maps is wanted. To get over this dilemma we propose a new post-acquisition visual representation for LSCI perfusion data using the so-called generalized differences (GD) algorithm. From a stack of perfusion images, the procedure leads to a new single image with the same spatial resolution as the original images and this new image reflects perfusion changes. The algorithm is herein applied on simulated stacks of images and on experimental LSCI perfusion data acquired in three different situations with a commercialized laser speckle contrast imager. The results show that the GD algorithm provides a new way of visualizing LSCI perfusion data., (Copyright © 2015 Elsevier Inc. All rights reserved.)

Laser speckle contrast imaging (LSCI) is a full-field imaging modality to monitor microvascular blood flow. It is able to give images with high temporal and spatial resolutions. However, when the skin is studied, the interpretation of the bidimensional data may be difficult. This is why an averaging of the perfusion values in regions of interest is often performed and the result is followed in time, reducing the data to monodimensional time series. In order to avoid such a procedure (that leads to a loss of the spatial resolution), we propose to extract patterns from LSCI data and to compare these patterns for two physiological states in healthy subjects: at rest and at the peak of acetylcholine-induced perfusion peak. For this purpose, the recent multi-dimensional complete ensemble empirical mode decomposition with adaptive noise (MCEEMDAN) algorithm is applied to LSCI data. The results show that the intrinsic mode functions and residue given by MCEEMDAN show different patterns for the two physiological states. The images, as bidimensional data, can therefore be processed to reveal microvascular perfusion patterns, hidden in the images themselves. This work is therefore a feasibility study before analyzing data in patients with microvascular dysfunctions.