Amira Zaylaa

Coarse-Grained Multifractality Analysis Based on Structure Function Measurements to Discriminate Healthy from Distressed Foetuses

This paper proposes a combined coarse-grained multifractal method to discriminate between distressed and normal foetuses. The coarse-graining operation was performed by means of a coarse-grained procedure and the multifractal operation was based on a structure function. The proposed method was evaluated by one hundred recordings including eighty normal foetuses and twenty distressed foetuses. We found that it was possible to discriminate between distressed and normal foetuses using the Hurst exponent, singularity, and Holder spectra.

n-Order and maximum fuzzy similarity entropy for discrimination of signals of different complexity

This paper presents two new concepts for discrimination of signals of different complexity. The first focused initially on solving the problem of setting entropy descriptors by varying the pattern size instead of the tolerance. This led to the search for the optimal pattern size that maximized the similarity entropy. The second paradigm was based on the n-order similarity entropy that encompasses the 1-order similarity entropy. To improve the statistical stability, n-order fuzzy similarity entropy was proposed. Fractional Brownian motion was simulated to validate the different methods proposed, and fetal heart rate signals were used to discriminate normal from abnormal fetuses. In all cases, it was found that it was possible to discriminate time series of different complexity such as fractional Brownian motion and fetal heart rate signals. The best levels of performance in terms of sensitivity (90%) and specificity (90%) were obtained with the n-order fuzzy similarity entropy. However, it was shown that the optimal pattern size and the maximum similarity measurement were related to intrinsic features of the time series.

Reducing sojourn points from recurrence plots to improve transition detection

The analysis of biomedical signals demonstrating complexity through recurrence plots is challenging. Quantification of recurrences is often biased by sojourn points that hide dynamic transitions. To overcome this problem, time series have previously been embedded at high dimensions. However, no one has quantified the elimination of sojourn points and rate of detection, nor the enhancement of transition detection has been investigated. This paper reports our on-going efforts to improve the detection of dynamic transitions from logistic maps and fetal hearts by reducing sojourn points. Three signal-based recurrence plots were developed, i.e. embedded with specific settings, derivative-based and m-time pattern. Determinism, cross-determinism and percentage of reduced sojourn points were computed to detect transitions. For logistic maps, an increase of 50% and 34.3% in sensitivity of detection over alternatives was achieved by m-time pattern and embedded recurrence plots with specific settings, respectively, and with a 100% specificity. For fetal heart rates, embedded recurrence plots with specific settings provided the best performance, followed by derivative-based recurrence plot, then unembedded recurrence plot using the determinism parameter. The relative errors between healthy and distressed fetuses were 153%, 95% and 91%. More than 50% of sojourn points were eliminated, allowing better detection of heart transitions triggered by gaseous exchange factors. This could be significant in improving the diagnosis of fetal state.

Delta-fuzzy similarity entropy to discriminate healthy from sick fetus

This paper deals with the discrimination between suffering and healthy fetuses, by means of a delta-fuzzy-similarity entropy. This new descriptor of complexity is based on the derivative of the fuzzy-similarity entropy. It was tested on fetal heart rate time-series and compared to the approximated and similarity entropies. The main outcome was the possibility to improve 10% the specificity and the sensitivity as compared to approximate entropy. This very good performance confirms that the new descriptor can be a valuable alternative as compared to other standard descriptors.

Advanced Discrimination between Healthy and Intrauterine Growth Restricted Fetuses by Unbiased Recurrence Plots

Intrauterine Growth Restriction, the poor growth of the fetus during pregnancy is a medical difficulty exposing the fetus to severe complications which could lead to its death. The discrimination of such medical problem is still challenging. This work aims to improve the discrimination between Intrauterine Growth Restricted fetuses from healthy fetuses, by comparing Unbiased Recurrence Plot analysis of their heart rates to standard recurrence plots. It also aims to offer the ultimate recurrence quantification parameter that leads the best discrimination. Entropy parameter, determinism, cross-determinism, recurrence rate and percentage of reduced sojourn points are computed from Unbiased Recurrence Plots. The evaluation of the effectiveness of discrimination is carried out using the sensitivity, specificity, accuracy and precision and Cohen’s kappa coefficient from the recurrence quantification analysis, and is compared to standard recurrence plots. Experimental results showed that the cross-determinism parameter is the utmost parameter providing a high discriminative power (86.5%) and the maximum relative separation between the two fetal heart rate sets (42.6%), and minimum standard deviation (± 0.12%). Both recurrence rate and entropy parameters are less active than cross-determinism but they are still adequate for Intrauterine Growth restricted fetuses classification. These results pave the way for highly sensitive classification devices. However, the detection of other medical problems should be further investigated, and deeper link of quantification parameters to distress diagnosis should be considered in order to understand the underlying physiological processes.

Entropy complexity analysis of electroencephalographic signals during pre-ictal, seizure and post-ictal brain events

Epileptic seizures reflect runaway excitation that severely hinders normal brain functions. With EEG recordings reflecting real-time brain activity, it is essential to both predict seizures and improve the classification of seizures in EEG signs. Towards this aim, nonlinear tools are strongly recommended to select the seizure-sensitive features prior to classification. However, the choice of the feature remains challenging. With the multitude of entropy parameters available in literature, and in order to perform a judicious selection of features that are fed to classifiers, this paper presents a comparative study of a host of candidate promising feature extraction techniques. Four entropy features namely Approximate Entropy, Sample Entropy and Renyi entropy of order 2 and Renyi entropy of order 3, were implemented as the standard techniques. Three kernel-based features namely Triangular Entropy, Spherical Entropy and Cauchy entropy were implemented. The former and latter entropies were computed from EEG recordings during induced seizures in three distinct phases: the pre-ictal (pre-seizure) phase, the ictal (seizure) phase, and the post-ictal (post-seizure) phase. Results showed that, among kernel-based methods, Spherical entropy features exhibited the largest parameter sensitivity to (Seizure-Normal) phase changes with the highest normalized relative separation (100%). The sample entropy feature in turn showed the most sensitive to EEG phase changes with the highest relative separation (94.85%), among the studied entropy alternatives.

Cascade of nonlinear entropy and statistics to discriminate fetal heart rates

Fetal heart rate discrimination is an evolving field in biomedical engineering with many efforts dedicated to avoid preterm deliveries by way of improving fetus monitoring methods and devices. Entropy analysis is a nonlinear signal analysis technique that has been progressively developed to improve the discriminability of a several physiological signals, with Kernel based entropy parameters (KBEPs) found advantageous over standard techniques. This study is the first to apply KBEPs to analyze fetal heart rates. Specifically, it explores the usability of the cutting-edge nonlinear KBEPs in discriminating between healthy fetuses and fetuses under distress. The database used in this study comprises 50 healthy and 50 distressed fetal heart rate signals with severe intrauterine growth restriction. The Cascade analysis investigates six kernel based entropy measures on fetal heart rates discrimination, and compares them to four standard entropies. The study presents a statistical evaluation of the discrimination power of each parameter (paired t-test statistics and distribution spread). Simulation results showed that the distribution ranges in 80% of the entropy parameters in the distressed heart group are higher than those in the healthy control group. Moreover, the results show that it is advantageous to choose Circular entropy then Cauchy entropy (p <; 0.001) over the standard techniques, in order to discriminate fetal heart rates.

Seizure Initiation with Focal Electrically Administered Seizure Therapy (FEAST)

Background: Late Onset Alzheimer’s Disease (LOAD) is one of the most common debilitating causes of dementia worldwide with heritability estimates ranging from 50 – 70%. Genome-wide association studies (GWAS) have identified more than 20 genetic loci in addition to APOEe4 that are associated with increased risk for LOAD. While most of these genes have weak effects, using a polygenic risk profile score (RPS) approach – a method that allows exploration of the influence of the cumulative effect of risk alleles we and others have shown the negative influence of LOAD risk genes on brain structure (Chauhan et al., 2015) and function (Xiao et al., 2015 HBM) even in healthy volunteers. Identifying mechanisms, particularly genetic mechanisms that confer resilience to the detrimental effect of LOAD related risk genes on brain structure and function could provide a viable avenue to identify novel therapeutic targets for LOAD. To that end, in the current study, we explored the role of polymorphisms in the gene encoding Reelin (RELN), a glycoprotein that has been shown to be critical for neuronal development and synaptic plasticity (Kramer et al. 2011), on the detrimental effect of LOAD RPS on hippocampal function. Studies have shown that normal RELN levels are necessary to prevent abnormal phosphorylation of tau (Ohkubo et al., 2003) and beta-amyloidinduced suppression of long term potentiation and NMDA receptors (Durakoglugil et al., 2009). Methods: BOLD functional MRI images (GE 3 T MRI scanner, TR/TE 1⁄4 2000/28ms, flip angle 1⁄4 90 deg, FOV 1⁄4 64x64, 24 axial slices, 170 volumes) were collected for 265 right-handed Caucasian healthy volunteers (116 male, 149 female) from the age of 18 to 86 years (SD 1⁄4 14.17) while they performed a simple declarative memory task (SDMT). Images were motion-corrected, normalized to MNI space, and spatially smoothed (8mm FWHM) using SPM5. Odd’s ratios of 22 independent SNPs, with Po1 10-5 in Hollingworth’s metaanalysis1 comprising four Alzheimer’s disease GWAS datasets (GERAD1, EADI1, TGEN1, ADNI), spanning the regions of ABCA7, APOC4, APOE, BCAM, BCL3, BIN1, C16orf88, CDK1, CEACAM1E, CLPTMI, CLU, CNTN5, CR1, CR2, CUX2, EXOC3L2, IQCK, LRRC68, MS4A4A, MS4A4E, MS4A6A, PICALM, PVR, PVRL2, and TOMM40 genes, were used to calculate the RPS for each individual subject using the approach described by Purcell et al.3. Association between RPS and hippocampal activation during the neutral encoding phase of the SDMT was tested using SPM12. To control for population stratification, 5 MDS components based on 8M SNP genotypes from a GWAS analysis extracted with EIGENSOFT5.01 were included in the analysis as covariates along with age, gender, SNAV, and genotyping batch labels. A region of interest analysis was performed using bilateral hippo-parahippocampal masks from the Anatomical Automatic Labeling Atlas. Influence of RELN on association between LOAD related AD RPS and hippocampal activation was examined separately for five independent Reelin polymorphisms (rs736707, rs362691, rs7341475, rs6943822, and rs4298437.) previously implicated in Alzheimer’s disease or Autism Spectrum Disease using flexible factorial analysis in SPM12. Results: fMRI analysis showed a significant negative correlation between LOAD RPS and hippocampal activation (left: PFWE_corrected 1⁄4 0.005, MNI coordinates x 1⁄4 -39, y 1⁄4 -24, z 1⁄4 -12, right: PFWE_corrected 1⁄4 0.139, Puncorrectedo0.001, MNI coordinates x 1⁄4 39, y 1⁄4 -18, z 1⁄4 -18) during the neutral encoding phase of SDMT. There were no significant positive correlations. In addition, there was a significant interactive effect (left: PFWE_corrected 1⁄4 0.076, MNI coordinates x 1⁄4 -30, y 1⁄4 -30, z 1⁄4 -6, right: PFWE_corrected 1⁄4 0.368, Puncorrected1⁄4 0.002, MNI coordinates x 1⁄4 27, y 1⁄4 -33, z 1⁄4 -9) of rs362691 genotype (a G-C missense variant) and LOAD RPS on activation. Furthermore, in the left hippocampus, minor allele C carriers (N1⁄4 56) showed a significant negative relationship (r1⁄4 -0.47, p1⁄4 0.0002, post-hoc analysis in R) between RPS and hippocampal activation, while the major allele G homozygotes (N1⁄4 208) showed no such relationship (r1⁄4 0.0071, p1⁄4 0.9186). None of the other RELN polymorphisms tested showed a significant effect. Conclusions: Our results, while showing a cumulative deleterious effect of several LOAD related risk genes on hippocampal function in healthy volunteers, also illustrate that this relationship is modulated by a missense SNP (rs362691) in the RELN gene. In particular, only the minor allele C carriers show a significant negative relationship between RPS and hippocampal function suggesting that homozygosity for the G allele in this polymorphism could potentially confer a protective effect.

Comparison between fresh and fixed human biopsies using spectral and lifetime measurements: Fluorescence analysis using one and two photon excitations

The purpose of this study is to make a comparison between the fluorescence emissions of fresh extracted human biopsies and fixed human biopsies, in order to evaluate the impact of fixation on autofluoresence signal. Our group is developing an endomicroscope to image brain tissues in-vivo, however to date, in order to validate our technology the easiest type of samples we can access are fixed samples. However, the fixation is still challenging. For that, we aim through this study to determine whether we should pursue to work on fixed samples or we should shift to work on fresh biopsies. Data were collected on spectroscopic, lifetime measurement and fluorescence imaging set-ups with visible and two-photon excitations wavelengths. Five fresh and five fixed samples are involved in the experiment. Endogenous fluorescence of fixed biopsies were calculated. Experimental results reveal that at 405 nm and 810 nm, the fresh samples have an intensity of fluorescence two times higher than that of fixed samples. However, for each fluorophore and each excitation wavelength, the lifetime for fresh samples is shorter than that for fixed samples. Still, further studies and investigations involving the comparison between different samples are required to strengthen our findings.

A Handy Preterm Infant Incubator for Providing Intensive Care: Simulation, 3D Printed Prototype, and Evaluation

Preterm infants encounter an abrupt delivery before their complete maturity during the third trimester of pregnancy. Polls anticipate an increase in the rates of preterm infants for 2025, especially in middle- and low-income countries. Despite the abundance of intensive care methods for preterm infants, such as, but not limited to, commercial, transport, embrace warmer, radiant warmer, and Kangaroo Mother Care methods, they are either expensive, lack the most essential requirements or specifications, or lack the maternal-preterm bond. This drove us to carry this original research and innovative idea of developing a new 3D printed prototype of a Handy preterm infant incubator. We aim to provide the most indispensable intensive care with the lowest cost, to bestow low-income countries with the Handy incubators care, preserve the maternal -preterms bond, and diminish the rate of mortality. Biomedical features, electronics, and biocompatible materials were utilized. The design was simulated, the prototype was 3D printed, and the outcomes were tested and evaluated. Simulation results showed the best fit for the Handy incubators components. Experimental results showed the 3D-printed prototype and the time elapsed to obtain it. Evaluation results revealed that the overall performance of Kangaroo Mother Care and the embrace warmer was 75 ± 1.4% and 66.7 ± 1.5%, respectively, while the overall performance of our Handy incubator was 91.7 ± 1.6%, thereby our cost-effective Handy incubator surpassed existing intensive care methods. The future step is associating the Handy incubator with more specifications and advancements.