Abstract
The electroencephalogram (EEG) is often used for the diagnosis of diseases and functional disturbances in the brain. In this paper, new algorithms developed for the automatic detection of transients in EEG are described. The single spike, and spike and wave bursts, both of which are abnormal phenomena associated with epileptic activity are considered. The algorithms for detecting these transients were tested using real EEG data. The transient detection is enhanced by two classification algorithms: patient-independent analysis and patient-dependent analysis. In the patient-independent analysis, multiple reference templates are generated from a patient population and for the patient-dependent analysis, the spikes from the patient's own EEG recording is used as reference. The description of the algorithms and their performances are presented.
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