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Purpose: The occurrence of unwanted afterdischarges (ADs) impedes cortical stimulation for mapping purposes. We investigated the safety of several stimulation paradigms., Methods: We compared the incidence of ADs and behavioral responses of two stimulation frequencies (50 and 100 Hz), at two intensities (1 and 0.2 ms pulse widths)., Results: Stimulation with 100 Hz was more likely to cause ADs than 50 Hz, and stimulation using 1 ms pulse width was more likely to cause ADs than 0.2 ms., Conclusions: Stimulation using 50 Hz frequency with a pulse width of 0.2 ms might be safer during cortical mapping.

Electroencepholographs (EEGs) of neonatal seizures differ from those of children and adults. This study evaluated whether wavelet transform analysis, a nonstationary frequency analysis of EEG, can recognize and characterize neonatal seizures. Twenty-second segments were analyzed from 69 EEG seizures in 15 neonatal patients whose seizures lasted 10 seconds or longer. The wavelet transform results were examined, as were EEG seizure durations and dominant frequencies. The wavelet transform results were correlated with the occurrence, after an 18-month follow-up, of postneonatal seizures. Wavelet transform analysis identified 40 seizures (58%) with a "sustained dominant frequency component" that lasted 10 seconds or longer and 29 seizures without a sustained dominant frequency component. The mean seizure duration of the 40 seizures with sustained dominant frequency components was 63.3 seconds, longer than the mean duration (33.6 seconds) of the seizures without sustained dominant frequency components, P < 0.01. Eleven patients manifested postneonatal epileptic seizures. Fifty-two EEG seizures in these 11 patients revealed more sustained dominant frequency components (74%) than 17 seizures in the 4 patients without postneonatal seizures (only 12%), P < 0.05. Wavelet transform analysis can identify neonatal EEG seizures and characterize their epileptic components. The presence of sustained dominant frequency components may predict postneonatal epileptic seizures.

Objective: Extraoperative cortical localizing stimulation (LS) is a standard clinical tool used to assess brain function before epilepsy surgery. However, LS can produce unwanted afterdischarges (ADs). We previously have shown that brief pulses of electrical stimulation (BPS) can terminate ADs caused by cortical stimulation. Our objective was to assess whether wavelet-crosscorrelation analysis could help predict the conditions under which BPS would be most likely to terminate ADs., Methods: We used wavelet-crosscorrelation analysis to get wavelet-correlation coefficients (WCC), and determine time lag (TL) and absolute value of TL (ATL) between two electrodes. For Analysis-1, we compared WCC and ATL in epoch 1 which was before LS, epoch 2 which was after LS but before BPS, and epoch 3 which was after BPS. For Analysis 2, we compared WCC and ATL during epoch 1 under 4 conditions: epochs when ADs subsequently terminated within 2 s after the end of BPS (1A), terminated within 2-5 s (1B), did not terminate within 5 s (1C), and when ADs did not appear (1D)., Results: We found that BPS efficacy in terminating ADs was predicted by (1) low correlation and (2) slow propagation speed between electrode pairs in the 2-10 s period before stimulation., Conclusions: Wavelet-crosscorrelation analysis can help predict conditions during which BPS can abort ADs. It is possible that similar analyses could help predict when BPS or other interventions could abort clinical seizures.

A method of evaluating brain function using the metacomputer concept of the Globus system combined with a message-passing interface is described. The proposed method has the ability to exploit various geographically distributed resources and parallel computing linked to a high-technology medical instrumentation system, magnetoencephalography, to analyze the functional state of the brain. It is envisaged that the method will lead to the realization of an efficient telemedicine system for health care.

The main objectives of the preoperative evaluation of a patient with medically intractable epileptic seizures are localization of the foci and propagation of the epileptiform discharges. Electrocorticographic (ECoG) data of intractable focal epilepsy were analyzed using an AR model, wavelet analysis, and cross-correlation analysis. In order to derive the time-shifts, the cross correlations of the epileptiform discharges were calculated between electrodes for every unit of time. Further analyses were made by means of a set of contour maps of the time-shifts and sequential two- and three-dimensional visualizations of the time-shift maps in order to localize the epileptic foci and study their propagation process. Two types of foci and propagation were revealed in the results. In the first type, epileptiform discharges were generated at localized focal sites and spread quickly to other sites. In the second type, the foci of epileptiform discharges, which appeared soon after the former bursts, were localized at more than one site, and the discharges tended to spread more slowly. The findings suggest that epileptic phenomena can be caused by at least two kinds of mechanisms in one patient: in the former, the propagation might be mediated through synaptic projections, while in the latter, the extracellular diffusion of an excitatory factor might play an important role. In addition, our newly developed visualization technique for the localization of epileptic foci and the propagation of epileptiform discharges should prove useful in the study of epileptogenesis etiology.

The relationships of background EEG to epileptiform discharge development were studied in 9 epileptic patients having generalized spike and wave complexes (SWCs) with a maximum at the frontal location on either side. The instantaneous power spectrum of EEGs at F3, F4, C3, C4, P3, P4, O1, O2, T3 and T4 was estimated with wavelet transform. Subsequently, the similarity of power spectra between SWCs and background EEG was determined by use of Kullback-Leibler information and artificial neural network in each patient. Both similarity measures revealed that EEG patterns similar to SWCs occurred in the background activity just before SWCs at frontal locations. These findings suggested that these SWC-like EEG events occurred as poorly developed epileptiform discharges buried in the background activity.