Space-time adaptive processing for improved estimation of preictal seizure activity

Detection of precursory, seizure-related activity in electroencephalograms (EEG) is a clinically important and difficult problem in the field of epilepsy. Seizure detection methods often aim to identify specific features and correlations between preictal EEG signals that differentiate them from interictal/nonictal signals. Typically, these methods use information from nonictal EEGs to establish detection thresholds, and do not otherwise incorporate their characteristics into the detection. A space-time adaptive approach is proposed to improve detection of seizure-related preictal activity in scalp EEG, using multiple patient-specific baseline signals to optimize the estimate of the baseline covariance matrix. A simplified model of the preictal EEG is assumed, which describes this signal as a linear superposition of seizure-related activity and baseline activity (treated as an interference signal). It is shown that when an improved estimate of the baseline covariance is included in the preictal detector, the true positive rate increases significantly and also the false positive rate decreases significantly.