The human brain is an inherently complex and dynamic system. Even at rest, functional networks dynamically reconfigure in a well-organized way to warrant efficient communication between regions. However, precise characterization of this reconfiguration very fast time-scale (hundreds millisecond) during rest remains elusive. In study, we used dense electroencephalography data recorded task-free paradigm track the temporal dynamics spontaneous networks. Results obtained from network-based...

This paper intends to investigate stress level detection of a driver during real world driving experiment. is based on heart rate variability (HRV) analysis which derived from ECG signal and reflects autonomic nervous system state the human body. The alteration predicts drivers operation permits safe by possibility an early warning. stress, taking place driving, caused diverse factors such as changing mood, bio rhythm, fatigue, boredom or disease can prevent reaching inappropriate for...

Fall detection for elderly and patient is a very important service that has the potential of increasing autonomy elders while minimizing risks living alone. It been an active research topic due to fact health care industry big demand products technology fall systems. Owing recent rapid advancement in sensing wireless communication technologies, systems have become possible. They allow detecting events elderly, monitoring them, consequently providing necessary help whenever needed. This paper...

Objective: Emerging evidence shows that cognitive deficits in Alzheimer's disease (AD) are associated with disruptions brain functional connectivity.Thus, the identification of alterations AD networks has become a topic increasing interest.However, to what extent induces disruption balance local and global information processing human remains elusive.The main objective this study is explore dynamic topological changes terms network segregation integration.Approach: We used...

The brain is a large-scale complex network often referred to as the "connectome". Exploring dynamic behavior of connectome challenging issue both excellent time and space resolution required. In this context Magneto/Electroencephalography (M/EEG) are effective neuroimaging techniques allowing for analysis dynamics functional networks at scalp level and/or reconstructed sources. However, tool that can cover all processing steps identifying from M/EEG data still missing. paper, we report novel...

Numerous types of linear and nonlinear features have been extracted from the electrohysterogram (EHG) in order to classify labor pregnancy contractions. As a result, number available is now very large. The goal this study reduce by selecting only relevant ones which are useful for solving classification problem. This paper presents three methods feature subset selection that can be applied choose best subsets classifying contractions: an algorithm using Jeffrey divergence (JD) distance,...

Quantifying the similarity between two networks is critical in many applications. A number of algorithms have been proposed to compute graph similarity, mainly based on properties nodes and edges. Interestingly, most these ignore physical location nodes, which a key factor context brain involving spatially defined functional areas. In this paper, we present novel algorithm called "SimiNet" for measuring graphs whose are priori within 3D coordinate system. SimiNet provides quantified index...

Along with the study of brain activity evoked by external stimuli, past two decades witnessed an increased interest in characterizing spontaneous occurring during resting conditions. The identification connectivity patterns this so-called "resting-state" has been subject a great number electrophysiology-based studies, using Electro/Magneto-Encephalography (EEG/MEG) source method. However, no consensus reached yet regarding unified (if possible) analysis pipeline, and several involved...

Premature delivery is a leading cause of fetal death and morbidity, making the prediction treatment preterm contractions critical. The electrohysterographic (EHG) signal measures electrical activity controlling uterine contraction. Analyzing EHG features can provide valuable insights for labor detection. In this paper, we propose framework using simulated signals to identify sensitive connectivity. We focus on propagation during delivery, recorded by multiple electrodes. Simulated were...

Toward the goal of detecting preterm birth by characterizing events in uterine electromyogram (EMG), authors propose a method detection and classification this signal. Uterine EMG is considered as nonstationary signal authors' approach consists assuming piecewise stationarity using dynamic change detector with no priori knowledge parameters hypotheses on process state to be detected. The based cumulative sum (DCS) local generalized likelihood ratios associated multiscale decomposition...

Computer vision applications such as refocusing, segmentation and classification become one of the most advanced imaging services. Light Field (LF) systems provide a rich semantic information scene. Using dense set cameras microlens arrays (Plenoptic camera), direction each ray coming from scene toward LF capture system can be extracted represented by spatial angular coordinates. However, induces many drawbacks including large amount data produced complexity increase for representation. In...

The brain is a large-scale complex network often referred to as the “connectome”. Cognitive functions and information processing are mainly based on interactions between distant regions. However, most of ‘feature extraction’ methods used in context Brain Computer Interface (BCI) ignored possible functional relationships different signals recorded from distinct areas. In this paper, connectivity quantified by phase locking value (PLV) was introduced characterize evoked responses (ERPs)...

The human brain is a dynamic networked system that continually reconfigures its functional connectivity patterns over time. Thus, developing approaches able to adequately detect fast dynamics critical. Of particular interest are the methods analyze modular structure of networks, is, presence clusters regions densely interconnected. In this paper, we propose novel framework identify states dynamically fluctuate time during rest and task. We started by demonstrating feasibility relevance using...

Emerging evidence showed that major depressive disorder (MDD) is associated with disruptions of brain structural and functional networks, rather than impairment isolated region. Thus, connectome-based models capable predicting the depression severity at individual level can be clinically useful. Here, we applied a machine-learning approach to predict using resting-state networks derived from source-reconstructed Electroencephalography (EEG) signals. Using regression three independent EEG...

This research aims to define an integrated strategy for modelling legal norms in the criminal domain supporting reasoning. For this purpose, OWL-DL ontology is captured from texts, using a middle-out approach, and rules are then formalized based on ontology. The goal construct rule-based decision support system Lebanese domain, grounded integration of set logic which defined expressive ability SWRL rule language.

In this paper, an active impedance control strategy for a knee-joint orthosis is proposed to assist individuals suffering from lower-limb muscular weaknesses during the swing phase of walking activities. The goal decrease human effort required ensuring successful knee joint movement without sacrificing wearer's priority. study, gait-phase based desired admittance model designed by analyzing kinematic and kinetic characteristics shank-foot segment walking. Moreover, mechanical human/orthosis...

In this paper, we propose, implement, and analyze the structures of two keyed hash functions using Chaotic Neural Network (CNN). These are based on Sponge construction, they produce variants value lengths, i.e., 256 512 bits. The first structure is composed two-layered CNN, while second one formed by one-layered CNN a combination nonlinear functions. Indeed, proposed employ strong systems, precisely chaotic system neural network system. addition, study new methodology combining networks...