Biomedical subjects

A Naït-Ali

Publications and source records attributed to A Naït-Ali.

6 recordsLinked to original sources

PubMed2005

Optimized brainstem auditory evoked potentials estimation using simulated annealing.

In this paper, we use a new approach based on Simulated Annealing for estimating the BAEPs (brainstem auditory evoked potentials). Each BAEP is obtained through a hundred of responses to stimulations. In case of endocochlear pathologies, it has been assumed that these signals could be randomly delayed from a response to another one. In such cases, the application of the averaging method systematically leads to "smoothed" BAEPs, thus complicating both identification and interpretation operations. The method presented in this paper consists in minimizing a non linear criterion in order to obtain an alignment of the various responses, before averaging them. Simulated and experimental results are presented, and compared to those produced by the classical method.

Brain Stem↗

PubMed2004

Performance of a computer system for recording and analysing eye gaze position using an infrared light device.

Our aim is to develop a computer system in order to study the visual scanning and eye fixation during exploration of image. The system uses an infrared device to detect the horizontal and vertical eye movements. In this paper, our interest is concentrated mainly on the calibration procedure. More precisely, on the correction of the nonlinearity in the oculomotor for individual subject's eyes. An adaptive calibration algorithm is developed. The algorithm uses a polynomial model and the optimal correction is obtained by using a mean square error criterion. The paper presents also a method to correct head movements. Experiments show that the proposed approach is fast and accurate for the study of visual perception and recognition processes.

Algorithms↗

PubMed2004

BAEPs averaging analysis using autoregressive modelling.

OBJECTIVE: The present paper introduces a new perspective on the classical ensemble averaging which can be useful to analyse the Brainstem Auditory Evoked Potentials (BAEPs). The analysis of the dynamics, related to the BAEP, is performed directly after its acquisition from the electroencephalogram (EEG). METHODS: The method primarily consists of dynamically modelling the averaged potential, obtained during the acquisition mode. Each averaging of signal at a given instant is considered as an autoregressive (AR) process. RESULTS: It has been shown that the predicting error power of AR modelling can be useful to provide an efficient tool to analyse the BAEPs. It has also been shown that the method is capable of taking the non-stationarities of both the BAEP and the EEG into account. CONCLUSION: In order to validate our approach, the proposed technique has been implemented for both simulated and real signals. This approach can also be employed in the context of estimating other evoked potentials and shows rich promise for potential clinical applications in future.

Algorithms↗

PubMed2004

An autoregressive (AR) model applied to eye tremor movement, clinical application in schizophrenia.

In this study, we use a parametric autoregressive (AR) model to obtain descriptive features of eye tremor movement during fixation. The interest consists in analyzing model parameters to determine the information that can be used as indicator of specific pathophysiology underlying cerebral dysfunction in schizophrenic subjects. We have tested healthy volunteers and schizophrenic medicated and unmedicated patients, to evaluate the treatment effect. The AR model is applied to the eye tremor movement extracted from the eye position signal recorded when subjects are fixating a stationary target. The analysis of the model parameters shows distinct classes, corresponding to a population of subjects among the three kinds included in this study.

Eye Movements↗

PubMed2002

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.

Aged↗

PubMedDate not supplied

BAEP dynamic estimation in case of endocochlear pathologies using a time delay correction method.

Extraction of Brainstem Auditory Evoked Potentials (BAEPs) from the electroencephalogram (EEG) is generally difficult when both BAEP and EEG are non-stationary. In this paper we focus on the problem of BAEP non-stationarities, in particular those observed in some endocochlear pathologies assumed causing random delays of BAEPs due to an abnormal behaviour of the cochlea. The technique developed in this paper, called the Time Delay Correction (TDC) method, allows us to estimate the averaged BAEP by an optimal alignment of responses based on a correlation criterion. We demonstrate that the TDC method avoids wave smoothness, generally produced with the classical ensemble averaging method, especially in the case when the hypothesis of the time delay non-stationarity is verified. The TDC method is performed using simulated annealing (SA) algorithm, since the criterion to be optimized is nonlinear. Real signals recorded from pathological subjects are used to validate the model of non-stationarity.

Adult↗