Effect of data length on time delay and embedding dimension for calculating the Lyapunov exponent in walking.

The Lyapunov exponent (LyE) is a trending measure for characterizing gait stability. Previous studies have shown that data length has an effect on the resultant LyE, but the origin of why it changes is unknown. This study investigates if data length affects the choice of time delay and embedding dimension when reconstructing the phase space, which is a requirement for calculating the LyE.

Toward a Label-Free Electrochemical Impedance Immunosensor Design for Quantifying Cortisol in Tears.

Cortisol is a viable biomarker for monitoring physiological, occupational, and emotional stress and is normally present in tear fluid at approximately 40 nM, or higher as a result of stress. We present characterization and quantification of cortisol via several electrochemical methods versus the standard enzyme-linked immunosorbent assay, commonly known as ELISA. We also present a prototyped design of a disposable test strip and handheld sensor based on label-free electrochemical impedance spectroscopy to quantify cortisol levels in tear fluid within approximately 90 seconds.

Feasibility of Commercially Marketed Health Devices for Potential Clinical Application.

Concerns have been raised regarding the lack of validation on consumer-marketed health-monitoring devices. An investigation to characterize current health monitoring devices was carried out in the laboratory using widely accepted clinical and industry criteria. In total, 16 unique devices were examined.

Basins of attraction in human balance.

Falls are a recognized risk factor for unintentional injuries among older adults, accounting for a large proportion of fractures, emergency department visits, and urgent hospitalizations. Human balance and gait research traditionally uses linear or qualitative tests to assess and describe human motion; however, human motion is neither a simple nor a linear process. The objective of this research is to identify and to learn more about what factors affect balance using nonlinear dynamical techniques, such as basin boundaries.

Detecting and characterizing high-frequency oscillations in epilepsy: a case study of big data analysis.

We develop a framework to uncover and analyse dynamical anomalies from massive, nonlinear and non-stationary time series data. The framework consists of three steps: preprocessing of massive datasets to eliminate erroneous data segments, application of the empirical mode decomposition and Hilbert transform paradigm to obtain the fundamental components embedded in the time series at distinct time scales, and statistical/scaling analysis of the components. As a case study, we apply our framework to detecting and characterizing high-frequency oscillations (HFOs) from a big database of rat electroencephalogram recordings.

Feasibility in the development of a multi-marker detection platform.

A feasibility study for a label-free, multi-marker single sensor using electrochemical impedance spectroscopy (EIS), imaginary impedance, and a signal decoupling technique is reported. To our knowledge, this is the first reported attempt of using imaginary impedance for biomarker detection and multi-marker detection. The electrochemical responses of purified low and high density lipoproteins (LDL and HDL, respectively) were first individually characterized through the immobilization of their molecular recognition elements (MREs) onto gold disk electrodes (GDEs).

A chimeric path to neuronal synchronization.

Synchronization of neuronal activity is associated with neurological disorders such as epilepsy. This process of neuronal synchronization is not fully understood. To further our understanding, we have experimentally studied the progression of this synchronization from normal neuronal firing to full synchronization.

Unstable periodic orbits and noise in chaos computing.

Different methods to utilize the rich library of patterns and behaviors of a chaotic system have been proposed for doing computation or communication. Since a chaotic system is intrinsically unstable and its nearby orbits diverge exponentially from each other, special attention needs to be paid to the robustness against noise of chaos-based approaches to computation. In this paper unstable periodic orbits, which form the skeleton of any chaotic system, are employed to build a model for the chaotic system to measure the sensitivity of each orbit to noise, and to select the orbits whose symbolic representations are relatively robust against the existence of noise.

Chaos computing in terms of periodic orbits.

The complex dynamics of chaotic systems can perform computations. The parameters and/or the initial conditions of a dynamical system are the data inputs and the resulting system state is the output of the computation. By controlling how inputs are mapped to outputs, a specific function can be performed.

Stochastic amplification of calcium-activated potassium currents in Ca2+ microdomains.

Small conductance (SK) calcium-activated potassium channels are found in many tissues throughout the body and open in response to elevations in intracellular calcium. In hippocampal neurons, SK channels are spatially co-localized with L-Type calcium channels. Due to the restriction of calcium transients into microdomains, only a limited number of L-Type Ca(2+) channels can activate SK and, thus, stochastic gating becomes relevant.

Chaogates: morphing logic gates that exploit dynamical patterns.

Chaotic systems can yield a wide variety of patterns. Here we use this feature to generate all possible fundamental logic gate functions. This forms the basis of the design of a dynamical computing device, a chaogate, that can be rapidly morphed to become any desired logic gate.

Circadian control of neural excitability in an animal model of temporal lobe epilepsy.

We provide experimental evidence for the emerging imbalance in the firing activity of two distinct classes (type 1 and type 2) of population spikes recorded from the hippocampal area CA1 in an animal model of temporal lobe epilepsy. We show that during the latent period of epileptogenesis following status epilepticus inducing brain injury, there is a sustained increase in the firing rate of type 1 population spikes (PS1) with a concurrent decrease in the firing rate of type 2 population spikes (PS2). Both PS1 and PS2 firing rates are observed to follow a circadian rhythm and are in-phase in control rats.

Non-parametric early seizure detection in an animal model of temporal lobe epilepsy.

The performance of five non-parametric, univariate seizure detection schemes (embedding delay, Hurst scale, wavelet scale, nonlinear autocorrelation and variance energy) were evaluated as a function of the sampling rate of EEG recordings, the electrode types used for EEG acquisition, and the spatial location of the EEG electrodes in order to determine the applicability of the measures in real-time closed-loop seizure intervention. The criteria chosen for evaluating the performance were high statistical robustness (as determined through the sensitivity and the specificity of a given measure in detecting a seizure) and the lag in seizure detection with respect to the seizure onset time (as determined by visual inspection of the EEG signal by a trained epileptologist). An optimality index was designed to evaluate the overall performance of each measure.

Detection of high frequency oscillations with Teager energy in an animal model of limbic epilepsy.

High frequency oscillations (HFO) in limbic epilepsy represent a marked difference between abnormal and normal brain activity. Faced with the difficult of visually detecting HFOs in large amounts of intracranial EEG data, it is necessary to develop an automated process. This paper presents Teager Energy as a method of finding HFOs.

Using recurrence quantification analysis determinism for noise removal in cardiac optical mapping.

Selecting signal processing parameters in optical imaging by utilizing the change in Determinism, a measure introduced in Recurrence Quantification Analysis, provides a novel method using the change in residual noise Determinism for improving noise quantification and removal across signals exhibiting disparate underlying tissue pathologies. The method illustrates an improved process for selecting filtering parameters and how using measured signal-to-noise ratio alone can lead to improper parameter selection.

Stochastic resonance in mammalian neuronal networks.

We present stochastic resonance observed in the dynamics of neuronal networks from mammalian brain. Both sinusoidal signals and random noise were superimposed into an applied electric field. As the amplitude of the noise component was increased, an optimization (increase then decrease) in the signal-to-noise ratio of the network response to the sinusoidal signal was observed.

A method for visualization of ventricular fibrillation: Design of a cooled fiberoptically coupled image intensified CCD data acquisition system incorporating wavelet shrinkage based adaptive filtering.

The measurement of cardiac transmembrane potential changes with voltage sensitive dyes is in increasing use. Detection of these very small fluorescent alterations using large multiplexed arrays, such as charge coupled device (CCD) cameras at high sampling rates, has proven challenging and usually requires significant averaging to improve the signal-to-noise ratio. To minimize the damage of living tissue stained with voltage sensitive dyes, excitation photon exposure must be limited, with the inevitable consequence of diminishing the fluorescence that is generated.

Control and synchronization of chaos in high dimensional systems: Review of some recent results.

Controlling chaos and synchronization of chaos have evolved for a number of years as essentially two separate areas of research. Only recently it has been realized that both subjects share a common root in control theory. In addition, as limitations of low dimensional chaotic systems in modeling real world phenomena become increasingly apparent, investigations into the control and synchronization of high dimensional chaotic systems are beginning to attract more interest.

Maintenance of chaos in a computational model of a thermal pulse combustor.

The dynamics of a thermal pulse combustor model are examined. It is found that, as a parameter related to the fuel flow rate is varied, the combustor will undergo a transition from periodic pulsing to chaotic pulsing to a chaotic transient leading to flameout. Results from the numerical model are compared to those obtained from a laboratory-scale thermal pulse combustor.