César Alexandre Teixeira

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Combining multiple linear univariate features in one feature space and classifying the feature space using machine learning methods could predict epileptic seizures in patients suffering from refractory epilepsy. For each patient, a set of twenty-two linear univariate features were extracted from 6 electroencephalogram (EEG) signals to make a 132(More)
With a worldwide prevalence of about 1%, epilepsy is one of the most common serious brain diseases with profound physical, psychological and, social consequences. Characteristic symptoms are seizures caused by abnormally synchronized neuronal activity that can lead to temporary impairments of motor functions, perception, speech, memory or, consciousness.(More)
Subclinical seizures (SCS) have rarely been considered in the diagnosis and therapy of epilepsy and have not been systematically analyzed in studies on seizure prediction. Here, we investigate whether predictions of subclinical seizures are feasible and how their occurrence may affect the performance of prediction algorithms. Using the European database of(More)
Identiÿcation results for the shaft-speed dynamics of an aircraft gas turbine, under normal operation , are presented. As it has been found that the dynamics vary with the operating point, non-linear models are employed. Two diierent approaches are considered: NARX models, and neural network models, namely multilayer perceptrons, radial basis function(More)
From the very beginning the seizure prediction community faced problems concerning evaluation, standardization, and reproducibility of its studies. One of the main reasons for these shortcomings was the lack of access to high-quality long-term electroencephalography (EEG) data. In this article we present the EPILEPSIAE database, which was made publicly(More)
Digital signal processing of Electroencephalogram (EEG) can support the diagnosis and alarming for the benefit of humans. About one third of all epileptic patients suffer from refractory epilepsy ; seizure prediction based on the EEG information content is an area of intense activity since at least twenty years. In this paper we analyze the high dimensional(More)
The reduction of the number of EEG features to give as inputs to epilepsy seizure predictors is a needed step towards the development of a transportable device for real-time warning. This paper presents a comparative study of three feature selection methods, based on Support Vector Machines. Minimum-Redundancy Maximum-Relevance, Recursive Feature(More)
OBJECTIVE Prediction of epileptic seizures can improve the living conditions for refractory epilepsy patients. We aimed to improve sensitivity and specificity of prediction methods, and to reduce the number of false alarms. METHODS Relative combinations of sub-band spectral powers of electroencephalogram (EEG) recordings across all possible channel pairs(More)
Heart Rate Variability (HRV) analysis has gained much importance in recent years, as a technique to explore the activity of autonomic nervous system (ANS), and an important early marker for identifying different pathological conditions. Epilepsy is a disease which progressively involves cardiac autonomic activity. Our studies indicate that time domain,(More)
Changes in the spatio-temporal behavior of the brain electrical activity are believed to be associated to epileptic brain states. We propose a novel methodology to identify the different states of the epileptic brain, based on the topographic mapping of the time varying relative power of delta, theta, alpha, beta and gamma frequency sub-bands, estimated(More)