Brain maturation estimation using neural classifier.

Quantitative electroencephalographic (EEG) signal analysis has revealed itself as an important diagnostic tool in the last few years. Through the use of signal processing techniques, new quantitative representations of EEG data are obtained. To automate the diagnosis, a problem of supervised classification must be solved on these. Artificial Neural Networks provide an alternative to more traditional classifier systems for this task. The objective of this paper is to perform a comparison between several classifiers in a particular problem, the brain maturation prediction. The data preprocessing/feature extraction process and the methodology for making the comparison are described. Performance of the methods is evaluated in terms of estimated percentage of correctly classified subjects.

Automatic analysis of signals with symbolic content.

This paper presents a set of methods for helping in the analysis of signals with particular features that admit a symbolic description. The methodology is based on a general discrete model for a symbolic processing subsystem, which is fuzzyfied by means of a fuzzy inference system. In this framework a number of design problems have been approached. The curse of dimensionality problem and the specification of adequate membership functions are the main ones. In addition, other strategies, which make the design process simpler and more robust, are introduced. Their goals are automating the production of the rule base of the fuzzy system and composing complex systems from simpler subsystems under symbolic constrains. These techniques are applied to the analysis of wakefulness episodes in the sleep EEG. In order to solve the practical difficulty of finding remarkable situations from the outputs of the symbolic subsystems an unsupervised adaptive learning technique (FART network) has been applied.

[The combined application of visual evoked potentials and the quantified electroencephalogram improve discrimination of cerebral maturity]. / La aplicación conjunta del potencial evocado visual y el electroencefalograma cuantificado mejora la discriminación de la maduración cerebral.

INTRODUCTION AND OBJECTIVE: The aim of this job is to evaluate brain maturation by means of Electroencephalogram (EEG) and Visual Evoked Potentials stimulated with flash (VEP-flash) quantitative analysis techniques. MATERIAL AND METHODS: The transversal study is made on a sample of 96 subjects in which EEG and VEP-flash, first isolated and then joining both, are analyzed. The selection of spectral parameters was done taking care of all the subjects were selected in the sense of maximizing brain maturation discrimination. Multivariate analysis techniques for classifying subjects were used. EEG and VEP-flash variables were selected with the linear discriminant analysis. In the EEG case the variables take into account, as a reference, either the median of the power spectrum or either the time instant in which the spectral power in every band reaches its maximum value. In the joined EEG-VEP-flash the VEP variables which give more information were related with the slopes and distances between the basic peaks of the evoked response (N1, P1 and N2) and age. For brain maturation evaluation the variables in the occipital channels are sufficient, being those of the right hemisphere the most diagnostic significative ones. CONCLUSION: The joined use of EEG and VEP-flash means an improvement in the maturative level discrimination regarding to the isolated consideration of any of them. Variables obtained from the EEG-VEP-flash are enough for brain maturation evaluation.

[A study of cerebral maturity by means of quantitative analysis of the electroencephalogram]. / Estudio de la maduración cerebral mediante el análisis cuantitativo del electroencefalograma.

INTRODUCTION AND OBJECTIVE: The objective of this study is to evaluate cerebral maturity by means of quantitative analysis techniques applied to the electroencephalogram (EEG). MATERIAL AND METHODS: A transversal study of cerebral maturity was carried out in 403 persons who had undergone an EEG. A previous pilot study had been carried out of 103 persons. A series of spectral parameters of the EEG were selected so that all those studied were in the most similar conditions possible. Different frequency bands were analyzed choosing the one with best discrimination of the maturity aspect. Classification of the different levels of cerebral maturity was done with the help of multivariant analysis. The value of the median of the frequencies and the spectrum of relative potencies at the moment when a frequency band is at a maximum are the parameters which evolve best with age and best discriminate maturity Spectral analysis allows selection of the frequency bands most suitable to the problem. Working with two frequency bands is sufficient to evaluate cerebral maturity. RESULTS: The variables obtained in the occipital channels were sufficient for evaluation of cerebral maturity. Those of the right hemisphere were more significant for diagnosis. The occipital channels are the most relevant in the study of cerebral maturity. CONCLUSIONS: The neuronal network is the most efficient classifier for classification of different groups of maturity The next most efficient method is by quadratic discriminant analysis. Consideration of the variables, taking into account the factors of stability and regularity of the EEG signals improves discrimination with respect to the average of those recorded during the entire procedure.

Tools for acquisition, processing and knowledge-based diagnostic of the electroencephalogram and visual evoked potentials.

The objective of our research is to develop computer-based tools to automate the clinical evaluation of the electroencephalogram (EEG) and visual evoked potentials (VEP). This paper describes a set of solutions to support all the aspects regarding the standard procedures of the diagnosis in neurophysiology, including: (1) acquisition and real-time processing and compression of EEG and VEP signals, (2) real-time brain mapping of spectral powers, (3) classifier design, (4) automatic detection of morphologies through supervised neural networks. (5) signal analysis through fuzzy modelling, and (6) a knowledge based approach to classifier design.