Hidden focal EEG seizures during prolonged suppressions and high-amplitude bursts in early infantile epileptic encephalopathy

Electroencephalographic criteria for nonconvulsive status epilepticus: synopsis and comprehensive survey

There have been many attempts at defining the electroencephalography (EEG) characteristics of nonconvulsive status epilepticus (NCSE) without a universally accepted definition. This lack of consensus arises because the EEG expression of NCSE does not exist in isolation, but reflects status epilepticus under the variety of pathologic conditions that occur with age, cerebral development, encephalopathy, and epilepsy syndrome. Current NCSE definitions include "boundary conditions," in which electroencephalographic seizure activity occurs without apparent clinical seizures. Furthermore, what appears to one interpreter as status epilepticus, is not to another reader, reflecting the "art" of EEG interpretation. Seizures and epilepsy syndromes have undergone an evolution that has moved beyond a classification of focal or generalized conditions into a syndromic approach. It seems appropriate to make similar changes in the EEG analysis of the syndromes of NCSE. In effect, the literature on epilepsy classification has progressed to incorporate the different NCSE types with clinical descriptions, but the specific EEG evidence for these types is found largely in individual reports, and often by description only. NCSE classification of EEG patterns should derive from the aggregate of published EEG patterns in the respective clinical subtype, supported by an analysis of these EEG studies. The analysis that follows presents clinical descriptions and EEG patterns of NCSE in the neonatal period, infancy, childhood, adulthood, and late adulthood from a syndromic perspective based on age, encephalopathy, cerebral development, etiology, and syndrome. Proceeding from the proposed classification of status epilepticus syndromes in "Status epilepticus: its clinical features and treatment in children and adults" (published in 1994 by Cambridge University Press, New York), we have performed a systematic search for reports presenting EEG patterns of NCSE using the online medical search engine PubMed for 22 different search strategies. EEG patterns were reviewed by two board-certified epileptologists who reached consensus regarding presence of NCSE. From a total of 4,328 search results, 123 cases with corresponding EEG patterns could be allocated to underlying epilepsy syndromes. Typical characteristic, prominent electrographic patterns, and sequential arrangements are elucidated for the different NCSE syndromes. This compendium of patterns by NCSE syndrome classification with illustration of EEGs, and delineation of electroencephalographic features helps define the characteristics and semiologic borderlines among the types of NCSE.

Optimal evaluation of digital electroencephalograms

Currently available digital EEG equipment provides considerably greater opportunities for clinical data analysis than is generally appreciated especially when appropriate software is used. Data from 7 different laboratories that had been obtained for routine diagnostic evaluations on 7 different EEG instruments and stored on compact disks were investigated. Since the instruments do not filter the data at input, ultra slow activity down to 0.01 Hz is currently being recorded but the attenuation factor is instrument dependent. Nevertheless, relevant clinical information is potentially available in these data and needs to be explored. Several examples in regard to epilepsy are presented. Determination of seizure onset may depend on the frequencies that are examined. The use of appropriate filter settings and viewing windows for the clinical question to be answered is stressed. Differentiation between simple and complex spike wave discharges, as well as spread of spikes, can readily be achieved by expanding the time base to 1 or 2 seconds and placing a cursor on the peak of the negative spike. Latencies in the millisecond range can then become apparent. EEGs co-registered with MEG should be evaluated with the same software in order to allow an adequate assessment of the similarities and differences between electrical and magnetic activity. An example of a comparison of EEG, planar gradiometers and magnetometers for an averaged spike is shown.