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The aim of this study was to examine the magnetoencephalography (MEG) background activity in Alzheimer's disease (AD) using three embedding entropies: approximate entropy (ApEn), sample entropy (SampEn), and fuzzy entropy (FuzzyEn). These three methods measure the time series regularity. Five minutes of recording were acquired with a 148-channel whole-head magnetometer from 36 AD patients and 24 elderly...
Different regions in the resting brain exhibit non-stationary functional connectivity (FC) over time. In this paper, a simple and efficient framework of clustering the variability in FC of a rat's brain at rest is proposed. This clustering process reveals areas that are always connected with a chosen region, called seed voxel, along with the areas exhibiting variability in the FC. This addresses an...
Thalamus is a very important part of the human brain. It has been reported to act as a relay for the messaging taking place between the cortical and sub-cortical regions of the brain. In the present study, we analyze the functional network between both hemispheres of the brain with the focus on thalamus. We used conditional Granger causality (CGC) and time-resolved partial directed coherence (tPDC)...
Given a time series of data points, as obtained in biosignal monitoring, the change point problem poses the question of identifying times of sudden variations in the parameters of the underlying data distribution. We propose a method for extracting a discrete set of change points from directional data. Our method is based on a combination of the Bayesian change point model (CPM) and the Viterbi algorithm...
Resting-state fMRI (rs-fMRI) studies of the human brain have demonstrated that low-frequency fluctuations can define functionally relevant resting state networks (RSNs). The majority of these methods rely on Pearson's correlation for quantifying the functional connectivity between the time series from different regions. However, it is well-known that correlation is limited to quantifying only linear...
Crackles are adventitious and explosive respiratory sounds that can be classified as fine or coarse. These sounds are usually associated with cardiopulmonary diseases such as the chronic obstructive pulmonary disease. In this work seven different features were tested with the objective to identify the best subset of features that allows a robust detection of coarse crackles. Some of the features used...
We have developed numerical approaches to dynamical analysis of heart rates, measured as interbeat or RR, intervals, based on entropy and fluctuation analyses in a large data base of consecutive Holter monitor recordings. In Part I, we present a RR interval-based classifier that distinguishes normal sinus rhythm (NSR), atrial fibrillation (AF) and sinus rhythm with ectopy with an accuracy of 99%,...
Insomnia is a condition that affects the nervous and muscular system. Thirty percent of the population between 18 and 60 years suffers from insomnia. The effects of this disorder involve problems such as poor school or job performance and traffic accidents. In addition, patients with insomnia present changes in the cardiac function during sleep. Furthermore, the structure of electroencephalographic...
In this paper, we present a novel approach for functional network connectivity in fMRI resting activity using distance correlation. The proposed method accounts for nonlinear relationships between the resting state networks and can be used for both single subject and group level analyses. We showed that the new strategy improves the capacity of characterization of pathological populations, such as,...
High dimensional functional MRI data in combination with a low temporal resolution imposes computational limits on classical Granger Causality analyses with respect to a large-scale representations of functional interactions in the brain. To overcome these limitations and exploit information inherent in resulting brain connectivity networks at the large scale, we propose a multivariate Granger Causality...
A priori discrimination of high mortality risk amongst congestive heart failure patients constitutes an important clinical stake in cardiology and involves challenging analyses of the temporal dynamics of heart rate variability (HRV). The present contribution investigates the potential of a new multifractal formalism, constructed on wavelet p-leader coefficients, to help discrimination between survivor...
The interpretation and analysis of intrapartum fetal heart rate (FHR), enabling early detection of fetal acidosis, remains a challenging signal processing task. The ability of entropy rate measures, amongst other tools, to characterize temporal dynamics of FHR variability and to discriminate non-healthy fetuses has already been massively investigated. The present contribution aims first at illustrating...
Owing to the recent advances in multi-modal data analysis, the aim of the present study was to analyze the functional network of the brain which remained the same during the eyes-open (EO) and eyes-closed (EC) resting task. The simultaneously recorded electroencephalogram (EEG) and magnetoencephalogram (MEG) were used for this study, recorded from five distinct cortical regions of the brain. We focused...
Heart rate variability (HRV) has been studied as a non-invasive technique to characterize the autonomic nervous system (ANS) regulation of the heart. Non-linear methods based on chaos theory have been used during the last decades as markers for risk stratification. However, interpretation of these nonlinear methods in terms of sympathetic and parasympathetic activity is not fully established. In this...
The extraction method of classification feature is primary and core problem in all epileptic EEG detection algorithms, since it can seriously affect the performance of the detection algorithm. In this paper, a novel epileptic EEG feature extraction method based on the statistical parameter of weighted complex network is proposed. The EEG signal is first transformed into weighted network and the weight...
Entropy analysis of images are usually performed using Shannon entropy, which calculates the probability of occurrency of each gray level on the image. However, not only the pixel gray level but also the spatial distribution of pixels might be important for image analysis. On the other hand, sample entropy (SampEn) is an important tool for estimation of irregularity in time series, which calculates...
Gender related-differences in the autonomic regulation of the cardiovascular and cardiorespiratory systems have been studied mainly by hemodynamic responses during different physical stressors. In this study, the influence of gender on the autonomic response to an orthostatic challenge was investigated by obtaining the cardiovascular and cardiorespiratory coupling using the nonlinear technique known...
Due to the increasing elderly population and the extensive number of comorbidities that affect them, studies are required to determine future increments in admission to emergency departments. Some of these studies could focus on the relation between chronic diseases and breathing pattern in elderly patients. Variations in the fractal properties of respiratory signals can be associated with several...
We have proposed a novel solution to a fundamental problem encountered in implementing non-ingestion based medical adherence monitoring systems, namely, how to reliably identify pill medication intake. We show how wireless wearable devices with tri-axial accelerometer can be used to detect and classify hand gestures of users during solid-phase medication intake. Two devices were worn on the wrists...
In this work, an attempt has been made to differentiate sEMG signals under muscle fatigue and non-fatigue conditions using multiscale features. Signals are recorded from biceps brachii muscle of 50 normal adults during repetitive dynamic contractions. After prescribed preprocessing, each signal is divided into six segments out of which first and last segments are considered in this analysis. Multiscale...
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