Abstracts

Rapid identification of patients with independent bitemporal seizure onset zones could greatly reduce presurgical evaluation costs. We describe a novel method to identify patients with independent bilateral seizure foci based on a nonlinear characteristic of EEG signals. Signal nonlinear characteristic is quantified by the statistical difference of Short-Term Maximum Lyapunov Exponent (STLmax) values estimated from the original EEG signals and its surrogate dataset. Eight adults with temporal lobe epilepsy (5 with unilateral temporal onsets and 3 with independent bilateral onsets) were included in this study. Long-term EEG recordings from bilaterally placed depth and subdural electrodes (left and right temporal depth, subtemporal and orbitofrontal) were analyzed. Initial analyses involved the following steps: (1) each EEG signal was divided into 10.24 sec epochs and 10 surrogate EEG time series were generated, (2) STLmax values were estimated for each epoch of the original EEG signal and each of the 10 surrogate EEG signals, (3) T-index, derived from pair-T statistics, was calculated to measure the statistical difference in STLmax values between the original signal and mean values estimated from the 10 surrogate EEGs. Nested two-way ANOVA was applied to test the significance of the brain region effect (patients were random blocks; seizures were nested within patient). Significance level = 0.05 was applied for rejection of the null hypothesis. For each of the five patients with unilateral seizure onset, statistical test revealed that the nonlinear characteristics of EEG signals derived from mesial temporal electrodes within the seizure onset zone are significantly different from those from other areas (p[lt]0.01). In contrast, the nonlinear characteristics of EEG are not different (p[gt]0.05) among recording areas in all three patients who experienced bilateral seizure onsets. These differences were present throughout the recording, even before the first seizure was recorded. The results of this study suggest that it may be possible to efficiently and quantitatively determine whether a patient is likely to have unilateral temporal or independent bitemporal seizure onsets, based on analysis of the nonlinear characteristics of the EEG signal. In these patients, the findings were robust and persistent. Further studies in a large sample of patients will be required to determine whether or not this approach will have utility in selecting candidates for resective surgery. (Supported by NIH Grant RO1EB002089 and Department of Veterans Affairs.)