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Statistical analysis, power spectral density, and Lempel Ziv complexity, are used in a multi-parameter approach to analyze four temporal series obtained from the Electrocardiographic and Respiratory Flow signals of 126 patients on weaning trials. In which, 88 patients belong to successful group (SG), and 38 patients belong to failure group (FG), i.e. failed to maintain spontaneous breathing during...
Designing an effective classifier has been a challenging task in the previous methods proposed in the literature. In this paper, we apply a combination of feature selection algorithm and neural network classifier in order to recognize five types of white blood cells in the peripheral blood. For this purpose, first nucleus and cytoplasm are segmented using Gram-Schmidt method and snake algorithm, respectively;...
In this study, we analyze brain connectivity based on Granger causality computed from magnetoencephalographic (MEG) activity obtained at the resting state in eight autistic and eight normal subjects along with measures of network connectivity derived from graph theory in an attempt to understand how communication in a human brain network is affected by autism. A connectivity matrix was computed for...
With the aim to control a multiple degrees of freedom electromechanical devices, e.g., assistive robots, powered wheelchair, etc., this paper proposes a real-time multichannel surface electromyography classification scheme based on the coordination or synergies between a functional group of muscles: biceps brachii, triceps brachii, pronator teres, and brachioradialis. The muscular synergy is evaluated...
The paper describes a feature selection process applied to electrogastrogram (EGG) processing. The data set is formed by 42 EGG records from functional dyspeptic (FD) patients and 22 from healthy controls. A wrapper configuration classifier was implemented to discriminate between both classes. The aim of this work is to compare artificial neural networks (ANN) and support vector machines (SVM) when...
The brain is a biological system with dynamic interactions between its sub-systems. The complexity of this system poses a challenge for identifying functional networks underlying observed neural activity. Current imaging approaches index local neural activity very well, but there is an increasing need for methods that quantify the interaction between regional activations. In this paper, we focus on...
Since ECG is huge in size sending large volume data over resource constrained wireless networks is power consuming and will reduce the energy of nodes in Body Sensor Networks (BSN). Therefore, compression of ECGs and diagnosis of diseases from compressed ECGs will play key roles in enhancing the life-time of body sensor networks. Moreover, discrimination between ventricular Tachycardia and Ventricular...
Baseline neurodynamics are believed to play an important role in normal brain function. A potentially intrinsic property of the brain is the weak coupling between networks at rest, which enables it to be flexible, adapt, process novel stimuli, and learn. Brain regions become differentially coordinated in response to cognitive task and behavior demands and external stimuli. However, abnormally synchronized...
Graphical models are powerful tools to infer statistical relationships between simultaneously observed random variables. Here, we used Dynamic Bayesian Networks (DBN) to infer causal relationships between simultaneously recorded neurons in the rat somatosensory (barrel) cortex in response to whisker stimulation. DBNs attempt to explain the activity of the observed neurons by searching for the best...
Brain networks with energy-efficient hubs might support the high cognitive performance of humans and a better understanding of their organization is of relevance not only for studying normal brain development and plasticity but also neuropsychiatric disorders. Here we propose an ultra-fast method to map the distribution of the functional connectivity density (FCD) in the human brain. The method was...
Corticostriatal dynamics exhibit gross alterations over the course of natural motor learning, yet little is known about the role they play in neuroprosthetic tasks. We therefore investigated interactions between the striatum and primary motor cortex while rats learned to control a brain-machine interface. Striatal firing rates increased greatly from early to late in learning, suggesting that the striatum...
A custom, patient-specific unicompartmental knee replacement was developed using a unsupervised neural network trained on a database of healthy knee geometries. This custom implant was then compared to a conventional implant in terms of contact stress in a Finite Element Model. The custom implant experienced lower contact stresses at the tibiofemoral joint compared to the conventional implant. The...
In this work, we present a neural network (NN) based method designed for 3D rigid-body registration of FMRI time series, which relies on a limited number of Fourier coefficients of the images to be aligned. These coefficients, which are comprised in a small cubic neighborhood located at the first octant of a 3D Fourier space (including the DC component), are then fed into six NN during the learning...
The main goal of this study was to develop a new method of estimating the angle of the passively stretched ankle joint, based on structural muscle spindle models of the tibial and peroneal electroneurograms (ENG). Passive ramp-and-hold and alternating stretches of the ankle joint were performed in a rabbit. Simultaneously, two cuff electrodes were used to record the ENGs of peroneal and tibial nerves...
This methodological work is aimed at providing a Granger causality based approach to the study of neuronal networks development in vitro. The analysis procedure makes use of tools derived from statistics and network theory for accessing network development of in-vitro neuronal cultures from their electrical activity, recorded through Multi Electrode Arrays (MEAs). The preliminary results that will...
A closed-loop virtual arm (VA) model has been developed in SIMULINK environment by adding spinal reflex circuits and propriospinal neural networks to the open-loop VA model developed in early study. An improved virtual muscle model (VM4.0) is used to speed up simulation and to generate more precise recruitment of muscle force at low levels of muscle activation. Time delays in the reflex loops are...
This paper is concerned with the methods developed for extending the capabilities of a spherical vision camera system to allow detection of surrounding objects and whether or not they pose a danger for movement in that direction during autonomous navigation of a power wheelchair. A Point Grey Research (PGR) Ladybug2 spherical vision camera system was attached to the power wheelchair for surrounding...
We present a circuit architecture for compact analog VLSI implementation of the Izhikevich neuron model, which efficiently describes a wide variety of neuron spiking and bursting dynamics using two state variables and four adjustable parameters. Log-domain circuit design utilizing MOS transistors in subthreshold results in high energy efficiency, with less than 1 pJ of energy consumed per spike. We...
We present a methodology for detecting effective connections between simultaneously recorded neurons using an information transmission measure to identify the presence and direction of information flow from one neuron to another. Using simulated and experimentally-measured data, we evaluate the performance of our proposed method and compare it to the traditional transfer entropy approach. In simulations,...
A system for diagnosing health problems from gait patterns of elderly to support their independent living is proposed in this paper. Motion capture system, which consists of tags attached to the body and sensors situated in the apartment, is used to capture gait of elderly. Position of the tags is acquired by the sensors and the resulting time series of position coordinates are analyzed with machine...
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