Abstracts

The objective was to develop a program ([ldquo]SeizureGuard[rdquo]) that is ideal for recognizing/predicting EEG seizures, real-time, within the limited power resources of future, fully implanted therapeutic devices, like the hybrid neuroprosthesis. Instead of handling the EEG signals as a succession of waves, the SeizureGuard program decomposed these signals into vectors, describing each vector with its angle and magnitude. From the derived vector-stream, the program computed various neurobiologically relevant parameters, including inter-vector interval and vector-periodicity. These parameters were indexed according to their relevance to the characteristics of electrographic seizure-onsets. No complex numbers were used in the calculations. To validate the algorithm, a data miner utility program was utilized to extract continuous EEG signals from files generated with a Nicolet BMSI 6000 recording system. All recordings were obtained from temporal lobe epilepsy patients presurgically implanted with subdural strip- and grid-electrodes. The program accurately recognized subclinical EEG seizures within 1-2 sec from their onset. Artifacts and non-epileptic rhythmic discharges and large-amplitude waves were not falsely indicated. Interestingly, the occasional false-positive seizure detections by the program in the interictal phase actually indicated bursts of sharp waves/spikes that occurred in the recording channel(s) of seizure activity. The SeizureGuard program, utilizing a computationally inexpensive algorithm that decomposes the EEG waves into vectors, is suitable for recognizing the onsets of focal subclinical seizures and has the potential to indicate epileptiform events prior to these seizures. As such, it seems to be ideal for use in implanted seizure-controlling devices. (Supported by NYU/FACES.)