Adaptive estimation of latency changes in evoked potentials

The Switch in a Genetic Toggle System with Lévy Noise

A bistable toggle switch is a paradigmatic model in the field of biology. The dynamics of the system induced by Gaussian noise has been intensively investigated, but Gaussian noise cannot incorporate large bursts typically occurring in real experiments. This paper aims to examine effects of variations from one protein imposed by a non-Gaussian Lévy noise, which is able to describe even large jumps, on the coherent switch and the on/off switch via the steady-state probability density, the joint steady-state probability density, and the mean first passage time. We find that a large burst of one protein due to the Lévy noises can induce coherent switches even with small noise intensities in contrast to the Gaussian case which requires large intensities for this. The influences of the stability index, skewness parameter and noise intensity on the on/off switch are analyzed, leading to an adjustment of the concentrations of both proteins and a decision which stable point to stay most. The mean first passage times show complex effects under Lévy noise, especially the stability index and skewness parameter. Our results also imply that the presence of non-Gaussian Lévy noises has fundamentally changed the escape mechanism in such a system compared with Gaussian noise.

Gaussian wavelet transform and classifier to reliably estimate latency of multifocal visual evoked potentials (mfVEP)

This paper describes a method to reliably estimate latency of multifocal visual evoked potential (mfVEP) and a classifier to automatically separate reliable mfVEP traces from noisy traces. We also investigated which mfVEP peaks have reproducible latency across recording sessions. The proposed method performs cross-correlation between mfVEP traces and second order Gaussian wavelet kernels and measures the timing of the resulting peaks. These peak times offset by the wavelet kernel's peak time represents the mfVEP latency. The classifier algorithm performs an exhaustive series of leave-one-out classifications to find the champion mfVEP features which are most frequently selected to infer reliable traces from noisy traces. Monopolar mfVEP recording was performed on 10 subjects using the Accumap1™ system. Pattern-reversal protocol was used with 24 sectors and eccentricity upto 33°. A bipolar channel was recorded at midline with electrodes placed above and below the inion. The largest mfVEP peak and the immediate peak prior had the smallest latency variability across recording sessions, about ±2ms. The optimal classifier selected three champion features, namely, signal-to-noise ratio, the signal's peak magnitude response from 5 to 15Hz and the peak-to-peak amplitude of the trace between 70 and 250 ms. The classifier algorithm can separate reliable and noisy traces with a high success rate, typically 93%.

Signal-to-noise ratio of intraoperative tibial nerve somatosensory-evoked potentials

To reveal the intrinsic signal-to-noise ratio (SNR) of single-trial somatosensory-evoked potentials (SEP). SEP was recorded from 13 scoliosis patients during surgery. The power of SEP was estimated with least-square fitting to obtain the most accurate value and then to estimate the SNR of every trial of SEP. The SNR of cortical SEP from 13 cases presented individual difference among each other. According to the mean and standard deviation, the coefficients of variation of cortical and subcortical SEP were 4.2% and 23%, respectively. The SNR of SEP was estimated to be -24 +/- 1 dB in cortical SEP and -22 +/- 5 dB in subcortical SEP. The lowest SNR of individual case was found to be -30 dB in cortical SEP and -53 dB in subcortical SEP. The results showed that SNR of intraoperative SEP recordings varies from person to person and presents a higher variability in subcortical than that in cortical, with a broad range from -53 to -5 dB. The results from this study can be used to understand the nature of SEP signals, which could guide researchers and designers on SEP denoising method selection, extraction, and measurement, as well as equipment development.

A new vision on the averaging technique for the estimation of non-stationary Brainstem Auditory-Evoked Potentials: application of a metaheuristic method

The aim of this paper consists in highlighting the use of the averaging technique in some biomedical applications, such as evoked potentials (EP) extraction. We show that this technique, which is generally considered as classical, can be very efficient if the dynamic model of the signal to be estimated is a priori known. Therefore, using an appropriate model and under some specific conditions, one can show that the estimation can be performed efficiently even in case of a very low signal to noise ratio (SNR), which occurs when handling Brainstem Auditory-Evoked Potentials.

Advances in quantitative electroencephalogram analysis methods

Quantitative electroencephalogram (qEEG) plays a significant role in EEG-based clinical diagnosis and studies of brain function. In past decades, various qEEG methods have been extensively studied. This article provides a detailed review of the advances in this field. qEEG methods are generally classified into linear and nonlinear approaches. The traditional qEEG approach is based on spectrum analysis, which hypothesizes that the EEG is a stationary process. EEG signals are nonstationary and nonlinear, especially in some pathological conditions. Various time-frequency representations and time-dependent measures have been proposed to address those transient and irregular events in EEG. With regard to the nonlinearity of EEG, higher order statistics and chaotic measures have been put forward. In characterizing the interactions across the cerebral cortex, an information theory-based measure such as mutual information is applied. To improve the spatial resolution, qEEG analysis has also been combined with medical imaging technology (e.g., CT, MR, and PET). With these advances, qEEG plays a very important role in basic research and clinical studies of brain injury, neurological disorders, epilepsy, sleep studies and consciousness, and brain function.

Phase estimation of visual evoked responses

The phase of visual evoked responses (VERs) is one of the basic parameters in functional diagnostics of the visual system. A new method for phase estimation of VERs based on the observer model in system identification is introduced. Simulated data show significantly less variance of estimation than actual estimators do. By means of the new estimator, the dynamics of the visual system according to selected optical stimuli has been analyzed.

Application of simulated annealing for estimating BAEPs in endocochlear pathologies

In this paper, we propose a new approach aimed at handling the temporal Brainstem Auditory Evoked Potentials (BAEPs) non-stationarity. It is pointed out that for some endocochlear pathologies, BAEPs could be randomly delayed from one response to another. This non-stationarity leads to smoothed BAEPs when applying ensemble averaging or any other technique based on BAEPs stationarity. In that case, waves identification is very difficult, sometimes impossible. The problem consists in estimating time delays. Knowing the distribution of delays allows subsequent study of the dynamic of the cochlea and, perhaps, identification of the nature of its pathology. The approach suggested in this paper is based on Simulated Annealing, used to minimize a non-linear criterion involving delays. This technique is advantageously compared to the non-corrected ensemble averaging method, using a set of simulated data based on a realistic model. As an illustration, results based on real signals recorded from two patients are presented and discussed at the end of the paper.

Latency change estimation for evoked potentials: a comparison of algorithms

Evoked potentials (EPs) have been widely used to quantify neurological system properties. Changes in EP latency may indicate impending neurological dysfunctions. This paper provides a review and a performance comparison of three classes of latency change estimation algorithms: correlation based, adaptive least mean square (LMS) based, and p-norm based algorithms. Data analysis based on computer simulated data and data from impact acceleration and hypoxia experiments were conducted. This concluded that correlation-based algorithms should only be used when the expected latency change is fixed and the noises are not correlated. While all adaptive LMS type algorithms were capable of tracking and estimating latency changes (fixed or variable) under Gaussian noise conditions, the direct LMS-based algorithm reduced the estimation error power given by traditional filter type LMS algorithms as much as 93%. When periodic interference was present, the frequency selective LMS algorithms outperformed other LMS-based algorithms and reduced of the estimation error power by 89%. Alpha-stable noise processes are better approximations of noises found in various EP analysis applications and adaptive p-norm based algorithms are found to be very robust under such noise conditions, eliminate erroneous abrupt latency changes other algorithms would have produced.

Latency change estimation for evoked potentials via frequency selective adaptive phase spectrum analyzer

This paper addresses the problem of detecting and estimating latency changes in evoked potentials (EP's). EP's have been widely used to quantify neurological system properties. Transient and time-varying changes in latency may indicate impending neurological injury. Traditional time averaging and correlation methods for EP latency estimation are inefficient under low signal-to-noise ratio (SNR) and/or strong periodic interference conditions. This paper proposes an adaptive phase spectral time delay estimation method to detect and estimate the time-varying latency changes when both the SNR and the signal-to-interference ratio (SIR) are low. A theoretical analysis and computer simulation demonstrate that the proposed method can track the time-varying latency changes effectively and accurately when both the SNR and the SIR are as low as -5 dB. The method is also suitable for real time detection and estimation of the latency changes.

Amplitudes and latencies of single-trial ERP's estimated by a maximum-likelihood method

The common approach in research on event-related electroencephalogram (EEG) potentials is to assume that the trigger-related signal is always the same and can be extracted from EEG background activity by simple averaging. To check the validity of this approach and to provide more exact results, latencies and amplitudes of components have to be estimated in single trials. Pham et al. applied a maximum-likelihood approach to solve the more general model which assumes that the signal hidden in EEG background activity has the same shape and amplitude but may vary in its latency from trial to trial. Extending their method we present a solution in which amplitude variability is also allowed. The utility of the solution to estimate the P3 component in single trials was investigated both by extensive pseudoreal simulations and in an application to real data. The simulations showed some advantage of the method over two other methods (Woody's method and peak-picking) commonly used in event-related potentials research. Application to real data provided a plausible description of single-trial sequential effects on the amplitude of the P3 component.

A system for tracking changes in the mid-latency evoked potential during anesthesia

This paper describes a method to measure changes in the mid-latency auditory evoked potential (MLAEP) during anesthesia. It is claimed that the position of the Nb-trough of the MLAEP indicates the level of consciousness. The component shows graded changes corresponding to the dose of anesthetic and it exhibits stable reproducible properties between different subjects. We propose a system that reduces the disturbances in an averaged MLAEP using fewer realizations than in the standard averaging procedure. The resulting smoothing error is reduced if the number of stimulus is decreased. Unfortunately, the variance of the waveform estimate is, thereby, increased. An improved method must be used in order to estimate the Nb-trough within a prescribed time interval of one minute. The procedure is based on inherent properties of the MLAEP and the noise. A simulation and examples of the performance on real data recorded during surgery are shown.