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A signal feature with low computational burden is presented as an efficient tool for seizure onset detection. The feature was evaluated over a total of 1,215 hours of intracranial EEG signal from 10 patients. Results confirmed this feature as being useful for seizure onset detection yielding an average delay of 4.1 seconds, 0.051 false positives per hour,(More)
We have developed and tested "off-line" an artificial neural network (ANN) that successfully detects epileptiform discharges (EDs) when trained on EEG records marked by an electroencephalographer (EEGer). The system was trained on both parameterized and raw EEG data and can process 49 channels of EEG data in real time on an 80486/33 MHz personal computer,(More)
We conducted a study to explore how electroencephalographers (EEGers) read EEGs and reach clinical impressions based upon them. Eight EEGers and a rule-based computerized "spike" detector marked epileptiform discharges ("EDs") in 12 test records. Of all marked events, 18% were marked by all readers and 38% were marked by only one reader. Readers agreed on(More)
Spontaneous energy fluctuations in human hippocampal EEG show prominent amplitude and temporal variability. Here we show hippocampal energy fluctuations often exhibit long-range temporal correlations with power-law scaling. In most cases this scaling behavior persisted on time scales in excess of 10 minutes, the maximum duration we could detect with our(More)
A technique of locating current dipoles in spherical conducting volumes by determining the location of the magnetic field maximum and inverting the magnetic field equations was developed and the expected localisation errors were predicted. AC current dipoles were placed in spheres of uniform conductivity. Each dipole's magnetic field was measured and its(More)