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Throughout the day, the human visual system acquires information using saccade and vergence eye movements. Previously, functional MRI (fMRI) experiments have shown both shared neural resources and spatial differentiation between these two systems. FMRI experiments can reveal which regions are activated within an experimental task but do not yield insight into how regions of interest (ROIs) interact...
In this paper, we propose a simple approach for detecting activated voxels in fMRI data by exploiting the inherent sparsity property of the BOLD signal. The proposed approach addresses the solution of the inverse problem induced by the General Linear Model through an l0-regularized Least Absolute Deviation (l0-LAD) regression method. Under this framework, the activated voxels are detected by a two-stages...
This work aims the development of a predictive strategy able to estimate future events with relevant impact in the cardiovascular status. Based on wavelet transform, a new time series similarity metric is introduced, which is capable to detect a pre-defined pattern in time series data. In addition, a methodology combining a wavelet scheme with state space multi-models is proposed to achieve the prediction...
The analysis of isometric force may provide early detection of certain types of neuropathology such as Parkinson's disease. Our long term goal is to determine if there are detectable differences between model parameters of healthy verses unhealthy individuals. As a first step toward our long-term goal, we studied 24 healthy young adults ages 18 through 24 years, both male and female. The experiments...
This study proposes a methodology to detect circaseptan (CS) rhythm in pulse rate (PR) data and to investigate the “Monday effect” in CS rhythm. Daily PR was collected from a middle-aged healthy working woman over one year. PR, SDNN index and sample entropy (SampEn) were chosen as the indexes of PR dynamics. In order to avoid interference from other biorhythms, ensemble empirical mode decomposition...
Results of heart rate variability analysis depend on the quality of the initial RR time series that is measured only in one lead of the ECG. This work shows that RR time series can subtly change from lead to lead so the choice of the analyzed lead is another source of uncertainty. The standard deviation of the differences of two RR time series obtained from different leads can change from 0.5 ms to...
The multiscale analysis of physiologic time series such as the RR interval time series has revealed that the entropy differs according to the scale. Furthermore, healthy subjects show different characteristics on the different time scales compared to patients. Instead of calculating entropies of the time series, the sequence of acceleration and deceleration of the instantaneous heart rate may also...
Atrial fibrillation (A-fib) is the most common cardiac arrhythmia. To effectively treat or prevent A-fib, automatic A-fib detection based on Electrocardiograph (ECG) monitoring is highly desirable. This paper reviews recently developed techniques for A-fib detection based on non-episodic surface ECG monitoring data. A-fib detection methods in the literature can be mainly classified into three categories:...
Local heating causes an increase in skin blood flow by activating sensory axon reflex and metabolic nitric oxide controls. It has been observed that the remote skin area without temperature changes also shows a slightly increase in blood flow. The responsible mechanism of this indirect vasodilation remains unclear. We hypothesized that the remote skin area will have enhanced synchronization of blood...
The aim of this study was to analyze the magnetoencephalography (MEG) background activity in Attention-Deficit/Hyperactivity Disorder (ADHD) using a regularity measure: sample entropy (SampEn). Five minutes of recording were acquired with a 148-channel whole-head magnetometer in 14 ADHD patients and 14 control subjects. Our results showed that ADHD patients' MEGs were more regular than controls' recordings...
Multielectrode neurophysiological recording and functional brain imaging produce massive quantities of data. Multivariate time series analysis provides the basic framework for analyzing the patterns of neural interactions in these data. Neural interactions are directional. Being able to assess the directionality of neuronal interactions is thus a highly desired capability for understanding the cooperative...
Insectivorous bats are able to locate and capture insects in complete darkness while flying at high speeds. They may consume hundreds of insects each night while avoiding obstacles in a complex environment. To investigate the processes associated with bat echolocation, we have developed instrumentation that allows us to record and visualize what a bat hears while flying through its natural environment...
The goal of this study was to evaluate the changes in heart rate variability (HRV) parameters due to a specific physical, mental or combined load. More specifically, the difference in effect between mental load and physical activity is studied. In addition, the effect of the combined physical and mental demand on the HRV parameters was examined and compared with the changes during the single task...
We have used two algorithms, wavelet phase coherence (WPC) and modulation index (MI) analysis to study frequency interactions in the human epileptic brain. Quantitative analyses were performed on intracranial electroencephalographic (iEEG) segments from three patients with neocortical epilepsy. Interelectrode coherence was measured using WPC and intraelectrode frequency interactions were analyzed...
The recently introduced multiscale entropy (MSE) method accounts for long range correlations over multiple time scales and can therefore reveal the complexity of biological signals. The existing MSE algorithm deals with scalar time series whereas multivariate time series are common in experimental and biological systems. To that cause, in this paper the MSE method is extended to the multivariate case...
Monitoring procedures are the basis to evaluate the clinical state of patients and to assess changes in their status, thus providing necessary interventions in time. To obtain this important objective it is necessary to integrate technological development with systems performing biomedical knowledge extraction and classification. Methods extracting non linear characteristics from HRV signal are presented...
Heart rate variability analysis is fast gaining acceptance as a potential non-invasive means of autonomic nervous system assessment in research as well as clinical domains. In this study, a new nonlinear analysis method is used to detect the degree of nonlinearity and stochastic nature of heart rate variability signals during two forms of meditation (Chi and Kundalini). The data obtained from an online...
We develop the methodology for hypothesis testing and model selection in nonhomogeneous Poisson processes, with an eye toward the application of modeling and variability detection in heart beat data. Modeling the process' non-constant rate function using templates of simple basis functions, we develop the generalized likelihood ratio statistic for a given template and a multiple testing scheme to...
To gain a deeper understanding of intermittent fluctuations observed in complex, real-world systems, we propose positive- or negative-directional non-Gaussian statistics. As a numerical example of asymmetric intermittent fluctuations, we heuristically introduce a random cascade-type model. Using our method, it is demonstrated that the asymmetric properties of heart rate variability depend on aging...
There has been a surge of research on electrocardiogram (ECG) signal based biometric for person identification. Though most of the existing studies claimed that ECG signal is unique to an individual and can be a viable biometric, one of the main difficulties for real-world applications of ECG biometric is the accuracy performance. To address this problem, this study proposes a PLR-DTW method for ECG...
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