Philippe Gallois

Multi-channel analysis of the EEG signals and statistic particularities for epileptic seizure forecast

In France, 5 to 8 people in 1000 suffer from epilepsy. An epileptic seizure is sudden, impressive, and is often followed by a loss of consciousness by the patient. The clinical studies have demonstrated that neuronal activity is responsible for these seizures. The electroencephalograms recorded by the doctors allow the visualization of the very beginning of the crises. The aim of our previous studies was to determine characteristics of the EEG signals that will allow us to forecast the seizures (Peyrodie et al., 2001). We reached the conclusion that using that using principal components analysis, we were able to find out some grapho-elements in the 2 first principal components that where leading us to highlight a state were patients are likely to make a seizure. A new challenge consists in trying to provide an interpretation of these grapho-elements. The first part is concerned in the explanation of the principal components analysis and the results it gave us. The second part is concerned with a statistical study of the principal components using a log-likelihood method. The third part is concerned with the use of independent component analysis to filter the EEG signals.

Filtering by optimal projection and application to automatic artifact removal from EEG

A new approach to filter multi-channel signals is presented, called filtering by optimal projection (FOP) in this paper. This approach is based on common spatial subspace decomposition (CSSD) theory. Moreover, an evolution of this method for non-stationary signals is also introduced which is called adaptative FOP (AFOP). As ICA, a filtering matrix is set up in the best way to remove artifacts with linear combination of channels. This filtering matrix is characterized by two subspaces. The first one is determined during a learning phase, by finding components maximizing the ratio signal over noise. The second one will be determined during a filtering phase, by reconstructing signals of a sliding window, by a least square method. These methods are completely automated and enable to filter independently numerous artifact types. Moreover, this filtering can be improved by applying this process on frequency band decomposed signals. Various tests have been made on electroencephalogram (EEG) signals in order to remove ocular and muscular activity while conserving pathological activity (slow waves, paroxysms). The results are compared with ICA filtering and medical inspection has been carried out to prove that this approach yields very good performance.

Improvements of Adaptive Filtering by Optimal Projection to filter different artifact types on long duration EEG recordings

Adaptive Filtering by Optimal Projection (AFOP) is an automatic method for reducing ocular and muscular artifacts on electro-encephalographic (EEG) recordings. This paper presents two additions to this method: an improvement of the stability of ocular artifact filtering and an adaptation of the method for filtering electrode artifacts. With these improvements, it is possible to reduce almost all the current types of artifacts, while preserving brain signals, particularly those characterising epilepsy. This generalised method consists of dividing the signal into several time-frequency windows, and in applying different spatial filters to each. Two steps are required to define one of these spatial filters: the first step consists of defining artifact spatial projection using the Common Spatial Pattern (CSP) method and the second consists of defining EEG spatial projection via regression. For this second step, a progressive orthogonalisation process is proposed to improve stability. This method has been tested on long-duration EEG recordings of epileptic patients. A neurologist quantified the ratio of removed artifacts and the ratio of preserved EEG. Among the 330 artifacted pages used for evaluation, readability was judged better for 78% of pages, equal for 20% of pages, and worse for 2%. Artifact amplitudes were reduced by 80% on average. At the same time, brain sources were preserved in amplitude from 70% to 95% depending on the type of waves (alpha, theta, delta, spikes, etc.). A blind comparison with manual Independent Component Analysis (ICA) was also realised. The results show that this method is competitive and useful for routine clinical practice.

A global approach for automatic artifact removal for standard EEG record.

The EEG signal is a record of the brain activity using multiple electrodes placed on the scalp. Unfortunately, it can be hardly contaminated by a lot of noises called artifacts. These latter can be generated by various actions such as eye blinks, eye movements or the skeletal muscle activities (jaw, forehead, ...). This study will focus on a global artifact removal method using independent component analysis (ICA) on signals cut in frequency bands. The interest of this method resides in automatizing the artifactual source identification and enables a global filtering of records using constant bases. A brief overview of the project will be made in order to introduce the method used. Next, the results will be presented and their validation will be discussed in the conclusion

BioSigPlot: An opensource tool for the visualization of multi-channel biomedical signals with Matlab

This paper presents a Matlab-based software (MathWorks inc.) called BioSigPlot for the visualization of multi-channel biomedical signals, particularly for the EEG. This tool is designed for researchers on both engineering and medicine who have to collaborate to visualize and analyze signals. It aims to provide a highly customizable interface for signal processing experimentation in order to plot several kinds of signals while integrating the common tools for physician. The main advantages compared to other existing programs are the multi-dataset displaying, the synchronization with video and the online processing. On top of that, this program uses object oriented programming, so that the interface can be controlled by both graphic controls and command lines. It can be used as EEGlab plug-in but, since it is not limited to EEG, it would be distributed separately. BioSigPlot is distributed free of charge (http://biosigplot.sourceforge.net), under the terms of GNU Public License for non-commercial use and open source development.

A Robust Method to Filter Various Types of Artifacts on Long Duration EEG Recordings

EEG is a system used to measure electrical brain activity using multiple electrodes placed on the scalp. Unfortunately, the signals can be easily contaminated by noises called artifacts. These can be generated by various actions such as eye blinks, eye movements, muscle activities or small electrode movements. This paper presents a global artifact removal method corresponding to an evolution of the AFOP method (Adaptive Filtering by Optimal Projection) in order to improve its stability. This evolution automatically filters ocular, muscular and heart beat artifacts. The results are validated on long duration EEG recordings containing pathological activities. An expert analysis shows that the cerebral signal is well conserved while a lot of artifacts are removed.

Evaluation of the AFOP/DAFOP method for automatic filtering of EEGs of patients with epilepsy.

Objective: Further developments in EEG monitoring necessitate new methods of filtering to eliminate artifacts, without transforming relevant signals. This article presents an automatic filtering of EEG recordings, based on a spatio-temporal method called Adaptive Filtering by Optimal Projection or Dual Adaptive Filtering by Optimal Projection. Evaluation of filtering methods is difficult, and comparisons between methods remain a challenge; here, we present a method to score the visual assessment of the EEG. The aim of this study was to evaluate an automatic filtering method, called Adaptive Filtering by Optimal Projection, improved by Dual Adaptive Filtering by Optimal Projection, of EEG recordings of patients with epilepsy. Methods: Two hundred forty-eight nonfiltered EEG segments of 20 seconds each were selected from 35 EEG recordings of 27 different patients by 3 clinical neurophysiologists based on their content. The reading quality as well as the proportions of artifacts and of cerebral activity removed after filtering were evaluated on a scale of 0 to 4. The mean square difference of amplitude before and after filtering was computed in specific spectral band. Results: The artifacts were largely removed (82% for muscular, 72% for ocular, and 71% for electrode artifacts). The readability was improved on an average by two points for pages containing epileptic seizures, and by one point for those containing alpha rhythms, slow waves, and spikes. After filtering, consistency tests showed a consensus (Spearman correlation [0.69–0.79]) on the removal of the artifact versus loss of information. The spectral analysis showed equivalent results (0.16% mean square difference in the alpha band). Conclusions: Our filtering method is effective in removing artifacts without altering relevant signals. The significance is that we evaluated a new automated method of filtering EEG that is easy to use for both for the analysis of routine EEG and in the field of epilepsy at large.

Dual adaptive filtering by optimal projection applied to filter muscle artifacts on EEG and comparative study.

Muscle artifacts constitute one of the major problems in electroencephalogram (EEG) examinations, particularly for the diagnosis of epilepsy, where pathological rhythms occur within the same frequency bands as those of artifacts. This paper proposes to use the method dual adaptive filtering by optimal projection (DAFOP) to automatically remove artifacts while preserving true cerebral signals. DAFOP is a two-step method. The first step consists in applying the common spatial pattern (CSP) method to two frequency windows to identify the slowest components which will be considered as cerebral sources. The two frequency windows are defined by optimizing convolutional filters. The second step consists in using a regression method to reconstruct the signal independently within various frequency windows. This method was evaluated by two neurologists on a selection of 114 pages with muscle artifacts, from 20 clinical recordings of awake and sleeping adults, subject to pathological signals and epileptic seizures. A blind comparison was then conducted with the canonical correlation analysis (CCA) method and conventional low-pass filtering at 30 Hz. The filtering rate was 84.3% for muscle artifacts with a 6.4% reduction of cerebral signals even for the fastest waves. DAFOP was found to be significantly more efficient than CCA and 30 Hz filters. The DAFOP method is fast and automatic and can be easily used in clinical EEG recordings.

P3-50 Etude des corrélations entre les tests neuropsychologiques et l’analyse linéaire et non linéaire de l’électroencéphalogramme au cours des démences

Introduction L’electroencephalogramme (EEG) presente un regain d’interet dans le diagnostic des demences depuis l’introduction des methodes mathematiques d’analyse. La plupart des etudes traite seulement de la maladie d’Alzheimer (MA) et ne teste que l’efficience cognitive globale. Objectifs Le but de ce travail est de determiner si l’analyse lineaire et non lineaire de l’EEG est correlee avec les differents tests neuropsychologiques dans les differentes demences. Methode 156 patients presentant une demence (50 MA, 36 demences vasculaires, 42 demences mixtes, 13 demences a corps de Lewy) ou un Mild Cognitive Impairment (n=15) beneficierent d’un EEG et de tests neuropsychologiques (MMSE, echelle de MATTIS, epreuve de Grober et Buschke, figure de Rey, denomination de Bachy, empans directs et inverses, test de barrage de chiffres, Trail Making Test, fluences categorielles et litterales, praxies reflexives). Il fut mene une analyse spectrale de puissance de l’EEG ainsi qu’une analyse non-lineaire a partir de la methode des recurrences proposee par Eckmann. Resultats et discussion L’analyse lineaire et non lineaire de l’EEG a permis d’etablir des correlations tres nombreuses non seulement avec les tests evaluant l’efficience cognitive globale (MMS et echelle de MATTIS) mais aussi avec chaque test neuropsychologique. Plus le test neuropsychologique et/ou l’efficience cognitive globale etait altere et plus l’EEG etait ralenti et contraint. Les correlations les plus nombreuses le furent avec l’analyse lineaire (73,6%) et principalement avec le rapport des frequences lentes (theta et delta) sur les rapides (alpha et beta) avec une discrete predominance pour l’hemisphere gauche et les regions fronto-temporales. Les aires cerebrales les plus correlees aux tests sont les regions fronto-temporales (52%), parieto-occipitales (24%), de la ligne mediane (15%) puis centrales (15%). Chaque test neuropsychologique etait correle a des alterations EEG situees sur de nombreuses electrodes sans reelle predominance regionale temoignant de la mise en jeu d’un grand nombre de reseaux neuronaux impliquant differents lobes cerebraux lors de la realisation d’une tâche cognitive particuliere. Conclusion La mise en evidence de nombreuses correlations entre les tests neuropsychologiques et les parametres d’analyse lineaire et non lineaire de l’EEG conforte la pertinence de l’utilisation de l’EEG pour le diagnostic et le suivi des demences.

P3-49 Diagnostic étiologique des démences à l’aide de l’analyse linéaire et non linéaire de l’électroencéphalogramme

Introduction L’electroencephalogramme (EEG) presente un regain d’interet dans le diagnostic des demences depuis l’introduction des methodes mathematiques d’analyse. La plupart des etudes realisees n’analyse que la maladie d’Alzheimer (MA). Objectifs Le but de ce travail est de determiner si l’analyse lineaire classique et non lineaire de l’EEG issue de la theorie du chaos est pertinente pour differencier les demences entre elles. Methode 156 patients presentant une demence (50 MA, 36 demences vasculaires (Dva), 42 demences mixtes (DM), 13 demences a corps de Lewy) ou un Mild Cognitive Impairment (n=15) beneficierent d’un EEG et de tests neuropsychologiques. Il fut realise une analyse spectrale de puissance de l’EEG ainsi qu’une analyse non-lineaire a partir de la methode des recurrences proposee par Eckmann. Des analyses discriminantes ont ete calculees pour classifier les differents types de demence. Les variables EEG retenues et celles issues du bilan neuropsychologique ont ete choisies en fonction des analyses de variance entre groupes. Resultats et discussion Les analyses discriminantes etablies permettaient de discriminer a 97,3% les Dva des DCL ; a 89,1% les DM des DCL ; a 87,3% les MA des Dva ; a 84,9% les Dva des DM ; a 78,9% les MA des Dva et a 62,5% les MA des DM. Les variables EEG les plus interessantes pour l’analyse discriminante etaient issues de l’analyse lineaire. Le seul parametre non lineaire utilise etait l’indice de Shannon. Les tests neuropsychologiques retenus etaient le rappel libre total en memoire episodique verbale et l’epreuve de denomination de Bachy. Seuls des parametres EEG ressortaient et ont donc etaient utilises pour distinguer les DCL des autres demences. L’analyse lineaire et non lineaire de l’EEG semble donc particulierement interessante pour diagnostiquer la DCL. Le pourcentage de patients bien classes etait majore par l’utilisation conjointe de variables neuropsychologique et EEG pour les autres demences. La demence la moins bien discriminee des autres est la DM. Cela peut etre explique par sa variabilite clinique et l’insuffisance des criteres diagnostiques actuels. Conclusion L’analyse lineaire et non lineaire de l’EEG represente un outil pour le diagnostic etiologique des demences.