Spectral EEG Analysis and Flunarizine Treatment in Migraine Patients

Cephalalgia ◽  
1988 ◽  
Vol 8 (8_suppl) ◽  
pp. 31-33 ◽  
Author(s):  
Rita Formisano ◽  
Nicola Martucci ◽  
Giovanni Fabbrini ◽  
Rosanna Cerbo ◽  
Roberto Proietti ◽  
...  

Spectral EEG analysis has been successfully utilized in previous studies on migraine patients. The aim of our study was to evaluate, by means of EEG mapping, potential correlations between the efficacy of flunarizine treatment in migraine patients and the EEG pattern recorded after chronic flunarizine therapy. Flunarizine was found to modify the non-specific EEG abnormalities of our migraine patients as well as evoke a positive clinical response.

Author(s):  
Greta Mainieri ◽  
Jean-Baptiste Maranci ◽  
Pierre Champetier ◽  
Smaranda Leu-Semenescu ◽  
Ana Gales ◽  
...  

Author(s):  
Prastiya Indra Gunawan ◽  
Darto Saharso

Background<br />Tuberculous meningitis (TBM) is a severe intracranial infection with fatal outcomes, permanent disabilities, and electroencephalographic (EEG) abnormalities. Seizures may occur in TBM. The EEG findings in TBM vary according to the site of the inflammatory process. There are few studies describing the EEG patterns and clinical manifestations of TBM. The objective of this study was to investigate the correlation between clinical findings and EEG patterns in children with TBM. <br /><br />Methods<br />A study of cross-sectional design using medical records was conducted on 12 children with TBM, with their EEG patterns classified as abnormal and normal. Clinical manifestations such as seizures, altered consciousness, headache or fever were collected. A positive cerebrospinal fluids Mycobacterium tuberculosis culture was considered to indicate definitive TBM. Abnormal EEG descriptions were classified into abnormal I, II or III. Correlation between EEG pattern and clinical manifestation were analyzed with Fisher’s exact test. <br /><br />Results<br />The study found cases of 12 children with TBM, the majority presenting with seizures, decreased consciousness and fever. Abnormal EEGs were found in 75% of children and 77% of them showed epileptogenic activities. The EEG results mostly described epileptogenic potentials in the frontotemporal region. There was a significant correlation between EEG abnormality and seizures in children with TBM (p&lt;0.05).<br /><br />Conclusions<br />The EEG pattern in children with TBM varies, and EEG abnormalities were more frequently localized in the frontotemporal region. Seizures were associated with EEG abnormalities in children with TBM. EEG abnormalities occurring simultaneously with seizures may predict the occurrence of seizures.


1982 ◽  
Vol 18 (9) ◽  
pp. 827-832 ◽  
Author(s):  
L. Schäffler ◽  
P. Imbach ◽  
A. Rüdeberg ◽  
F. Vassella ◽  
K. Karbowski

Cephalalgia ◽  
1987 ◽  
Vol 7 (6_suppl) ◽  
pp. 53-53 ◽  
Author(s):  
C. Delmer ◽  
D. Samson-Dollfus ◽  
B. Mihout ◽  
Y. Vaschalde ◽  
D. Parain ◽  
...  

1995 ◽  
Vol 4 (3) ◽  
pp. 171-183 ◽  
Author(s):  
Charles W. Anderson ◽  
Saikumar V. Devulapalli ◽  
Erik A. Stolz

EEG analysis has played a key role in the modeling of the brain's cortical dynamics, but relatively little effort has been devoted to developing EEG as a limited means of communication. If several mental states can be reliably distinguished by recognizing patterns in EEG, then a paralyzed person could communicate to a device such as a wheelchair by composing sequences of these mental states. EEG pattern recognition is a difficult problem and hinges on the success of finding representations of the EEG signals in which the patterns can be distinguished. In this article, we report on a study comparing three EEG representations, the unprocessed signals, a reduced-dimensional representation using the Karhunen – Loève transform, and a frequency-based representation. Classification is performed with a two-layer neural network implemented on a CNAPS server (128 processor, SIMD architecture) by Adaptive Solutions, Inc. Execution time comparisons show over a hundred-fold speed up over a Sun Sparc 10. The best classification accuracy on untrained samples is 73% using the frequency-based representation.


1992 ◽  
Vol 4 (3) ◽  
pp. 187-192 ◽  
Author(s):  
Peter Perros ◽  
Edwin S. Young ◽  
James J. Ritson ◽  
Greg W. Price ◽  
Peter Mann

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