title: Expert system approach to detection of epileptiform activity in the EEG.
abstract: An expert system for the automated detection of spikes and sharp waves in the EEG has been developed. The system consists of two distinct stages. The first is a feature extractor, written in the conventional procedural language Fortran, which uses parts of previously published spike-detection algorithms to produce a list of all spike-like occurrences in the EEG. The second stage, written in the production system language OPS5, reads the list and uses rules incorporating knowledge elicited from an electroencephalographer (EEGer) to confirm or exclude each of the possible spikes. Information such as the time of occurrence, polarity and channel relationship are used in this process. A summary of the detected epileptiform events is produced which is available to the EEGer in interpreting the EEG. The performance of the expert system is compared with an EEGer using a 320s segment from an EEG containing epileptiform activity. The system detected 19 events and missed seven (false negative) which the EEGer considered epileptiform. There were no false positive detections.
authors.forename: B L
authors.initials: BL
authors.lastname: Davey
authors.forename: W R
authors.initials: WR
authors.lastname: Fright
authors.forename: G J
authors.initials: GJ
authors.lastname: Carroll
authors.forename: R D
authors.initials: RD
authors.lastname: Jones
mesh_terms: D001698:Biomedical Engineering; D003936:Diagnosis, Computer-Assisted; D004569:Electroencephalography; D004827:Epilepsy / Q000145:classification / Q000175:diagnosis*; D005069:Evaluation Studies as Topic; D005103:Expert Systems; D006801:Humans