Endogenous opioids have been shown to produce beneficial effects in experimental stroke. To evaluate both neurophysiological and biochemical parameters, we induced massive cerebral ischemia in 11 rabbits according to the method standardized in our laboratory, using microspheres injected through the internal carotid artery. Binding studies were performed in the 11 embolized, in nine control, and...

Epilepsy and non-localized brain dysfunction have been invoked, among others, as underlying factors in borderline personality disorder. We have recorded 58 electroencephalograms in 20 borderline patients, first after complete drug washout and then under carbamazepine or placebo double-blind treatment. Taking into account only definite abnormal tracings, we found a 40% incidence of abnormal diff...

We propose a novel state space modelling approach to removing scanner-related artifacts from electroencephalograms recorded inside MR scanners. For this purpose, dynamical templates for the actual brain activity and the ballistocardiogram are obtained from a short piece of data recorded without fMRI scanning; dynamical templates for the scanner artifacts are obtained from data recorded during f...

Results: The electroencephalograms and magnetic resonance imaging (MRI) scans of our patients revealed features that have received little attention in previous studies. Of the 9 patients who were examined with electroencephalography, all 9 had seizures or other abnormalities, and 1 had nonconvulsive status epilepticus. Two of 6 patients who had MRIs showed substantia nigra edema. Finally, 2 (18...

This article deals with the problem of on-line wave detection in non-stationary signals, through a polynomial approximation based on genetics algorithms. This approximation is estimated by minimising a nonlinear error function, described by the circle equation. We apply this approach to detect sleep spindle waveforms in electroencephalograms (EEG) and R wave and ectopics beats in electrocardiog...

We describe a polynomial network technique developed for learning to classify clinical electroencephalograms (EEGs) presented by noisy features. Using an evolutionary strategy implemented within Group Method of Data Handling, we learn classification models which are comprehensively described by sets of short-term polynomials. The polynomial models were learnt to classify the EEGs recorded from ...

The awareness of anti-N-methyl-D-aspartate receptor (NMDAR) encephalitis has been increasing throughout the world. Typically, psychiatric symptoms (PS) initially occur, followed by the development of seizures, involuntary movements, autonomic instability, or central hypoventilation.

In this chapter we describe new neural-network techniques developed for visual mining clinical electroencephalograms (EEGs), the weak electrical potentials invoked by brain activity. These techniques exploit fruitful ideas of Group Method of Data Handling (GMDH). Section 2 briefly describes the standard neural-network techniques which are able to learn well-suited classification modes from data...

Electroencephalograms (EEGs) are brain waves, which are recorded using scalp electrodes. Generally, signal attenuate on recording and amplitude of the evoked potentials (EPs) are low when merged with the base brain waves. Therefore, mathematical tools are needed to analysis the time series (EEGs) to discover the EPs in the base EEGs. This paper reviews spectral analysis based on periodic amplit...

Fourier analysis has shown that human electroencephalography (EEG) signals mainly consist of frequencies below 80 Hz. Waves with a frequency below 13Hz are dominant during sleep. Here we find that the fractal dimensions of sleep EEG results in a frequency range of 2Hz to 13Hz have potential to be used as a continuous parameter to characterize sleep status. Our results show that during sleep, th...