Parham Ghorbanian

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Alzheimer’s disease (AD) is associated with deficits in a number of cognitive processes and executive functions. Moreover, abnormalities in the electroencephalogram (EEG) power spectrum develop with the progression of AD. These features have been traditionally characterized with montage recordings and conventional spectral analysis during resting(More)
OBJECTIVE In this paper a new nonlinear system identification approach is developed for dynamical quantification of cardiovascular regulation. This approach is specifically focused on the identification of the heart rate (HR) baroreflex mechanism. The principal objective of this paper is to improve the model accuracy in the estimation of HR by proposing a(More)
The aim of this study is to develop an algorithm to detect and classify six types of electrocardiogram (ECG) signal beats including normal beats (N), atrial premature beats (A), right bundle branch block beats (R), left bundle branch block beats (L), paced beats (P), and premature ventricular contraction beats (PVC or V) using a neural network classifier.(More)
In this study, electroencephalogram (EEG) signals obtained by a single-electrode device from 24 subjects - 10 with Alzheimer's disease (AD) and 14 age-matched Controls (CN) - were analyzed using Discrete Wavelet Transform (DWT). The focus of the study is to determine the discriminating EEG features of AD patients while subjected to cognitive and auditory(More)
In this paper, we review the recent results on stability and control for time-varying sets of nonlinear time-varying dynam-ical systems and utilize them for the problem of multi-vehicle coordinated motion in the context of obstacle avoidance where obstacles are approximated and enclosed by elliptic shapes. Specifically, we design distributed controllers for(More)
Coupled nonlinear oscillator models of EEG signals during resting eyes-closed and eyes-open conditions are presented based on Duffing-van der Pol oscillator dynamics. The frequency and information entropy contents of the output of the nonlinear model and the actual EEG signal is matched through an optimization algorithm. The framework is used to model and(More)
Objective: The aim of this study is to found how well one can characterize the location and extent of relatively compact infarcts using electrocardiographic evidence. Method: Here, we address a method base on behavior of some ECG's features, which are Q, amplitudes and ST dispersion. We call these Q and ST curves. At the first step, by plotting the(More)
We have developed a novel approach to elucidate several discriminating EEG features of Alzheimer’s disease. The approach is based on the use of a variety of continuous wavelet transforms, pairwise statistical tests with multiple comparison correction, and several decision tree algorithms, in order to choose the most prominent EEG features from a single(More)