Time division multiplexing based method for compressing ECG signals: application for normal and abnormal cases.

The proposed ECG compression method combines three major approaches based on time division multiplexing (TDM) and multilevel wavelet decomposition followed by parametrical modelling. Before applying these techniques, a pre-processing step is required, which consists of detecting and aligning different beats. Even though this compression method is regarded as a lossy method, we will show how a high compression ratio (CR) can be achieved by preserving the major medical information within the ECG. Several normal and abnormal signals from various databases are used to evaluate the performance of the proposed technique.

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.

[Hilbert transform-based modelling of EKG].

Recent developments in modeling methods on the non-linear and non-stationary data, such as electrocardiograms (ECG), have received large attention by the time-frequency analysts. The technique presented in this paper is based on parametrical modeling of the instantaneous module as well as instantaneous phase, estimated directly from each ECG beat. The estimation is adaptive since, the parameters related to a specific ECG beat are calculated from a given reference signal. In order to evaluate the performance of our technique, data recorded from the MIT-BIH arrhythmia database are used.

[ECG compression method by using multiple polynomial modelling: comparison with wavelet-changing technique].

The proposed ECG compression combined two approaches, ECG beat alignment and polynomial modeling. QRS complexes are firstly detected then aligned in order to reduce high frequency changes from beat to beat. These changes are modeled by means a polynomial projection. ECG from MIT-BIH database are used to evaluate the performances of the proposed technique. A comparison with the wavelet approach is performed by means the compression ratio (CR) versus the RRD curve.

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.

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.

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.

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.

[An approach to modeling the dynamics of vestibular myogenic potentials]. / Podkhod k modelirovaniiu dinamiki vestibuliarnykh vyzvannykh miogennykh potentsialov.

Some problems related with vertigo belong to saccular malfunctions. The purpose of this case study was to analyze the vestibular induced myogenous potentials (VIMP) isolated from responses to an acoustic stimulation of the internal ear. The suggested technique is based on modeling each average response as an autoregression process (AP). The spectral analysis of a singal, representing a temporal evolution of the AP parameters, was shown to be applicable for studying the VIMP time dynamics. The method can be used as an instrument in classifying the vestibular pathologies.

[Optimal estimation of temporal non-stationary signals by using simulated annealing: application to brainstem auditory evoked potentials]. / Optimal'naia otsenka nestatsionarnykh signalov s ispol'zovaniem metoda simulated annealing (model'noi "zakalki"): primenenie dlia zvukovykh vyzvannykh potentsialov stvola mozga.

A brainstem auditory evoked potential (BAEP) is an electrical signal evoked by neurological activity and recorded after stimulating acoustically the auditory system. A random delay of responses from a stimulated one to another one is one of the problems in estimating BAEPs. This leads to a smoothed estimated signal when applying the classical averaging method. The technique, presented in this paper, consists in using an optimal corrected averaging method, based on Simulated Annealing (SA), and in estimating a delay vector that maximizes a non-linear criterion. The optimal vector is used to correct the signal. BAEPs can be afterwards enhanced. The new method is advantageously compared with the classical averaging method.

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.