PubMed · 13294073
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L L SOREL. 1956. EEG classification sheet.. https://doi.org/10.1016/0013-4694(56)90042-6
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The authors reviewed the reports of all emergent EEG (EmEEG) performed in our hospital within 1 hour of the test being ordered over a period of 52 months. Two hundred sixty-one EmEEG (12.8% of all EEG) were performed. The most common reason to order the test was a change in mental status or coma (17.6%). Although EmEEG was ordered to rule out status epilepticus (SE) in 60.2% of cases, this diagnosis was made in only 10.7% of patients. The only independent predictor for SE was a history of cardiac or respiratory arrest (odds [95% CI], 6.8 [2.7 to 16.9]).
In this study an information-theoretic test for general Granger causality is used to identify couplings and information transport between different brain areas during epileptic activities. This method can distinguish information that is actually exchanged between two systems from that due to the response to a common signal or past history. This is achieved by an appropriate conditioning of probabilities. Statistical assessment of causality is made from a nonparametric bootstrap test, whereas nonlinearity is assessed by a comparison with a linearized version of the causality index. The framework proposed here provides a useful and model free test to characterize interactions in intracranial electroencephalography (EEG) signals.
Detrended fluctuation analysis has recently demonstrated the existence of two approximate temporal scaling regimes in locally detrended human electroencephalographic (EEG) fluctuations, and has suggested a connection between the location of the breakpoint between regimes and the alpha resonance near 10 Hz. It is shown here that these scalings can be explained in terms of the filtering of the underlying power spectrum implied by the detrending process. Using a recent physiologically based model of EEG generation, the main features of the scalings, and deviations from them, are related to the underlying physiology of dendritic propagation and muscle electrical activity, and it is concluded that the effects of such physiological features are usually clearer in spectra.