Extraction and analysis of early ictal activity in subdural electroencephalogram

Subdural electroencephalograms (SEEGs) are of great value in localizing primary epileptogenic regions in patients undergoing evaluation for focal resective epilepsy surgery. The data segments which contain a transition from interictal to ictal activity carry the most critical diagnostic information. Computer signal extraction within this transition period represents a challenging signal processing problem. In this work a two-step method is presented to extract early ictal activity. In the first step we employ a nonlinear signal decomposition technique in the wavelet domain to separate SEEG data into ictal and background components. In the second step we use time-frequency analysis and a novel integration algorithm to extract the desired information. Our experiments on clinically recorded data indicate that this method is highly effective allowing us to reveal important hidden features in the data which could not otherwise be observable.