title: EpiScan: online seizure detection for epilepsy monitoring units.
abstract: An online seizure detection algorithm for long-term EEG monitoring is presented, which is based on a periodic waveform analysis detecting rhythmic EEG patterns and an adaptation module automatically adjusting the algorithm to patient-specific EEG properties. The algorithm was evaluated using 4.300 hours of unselected EEG recordings from 48 patients with temporal lobe epilepsy. For 66% of the patients the algorithm detected 100% of the seizures. A mean sensitivity of 83% was achieved. An average of 7.2 false alarms within 24 hours for unselected EEG makes the algorithm attractive for epilepsy monitoring units.
authors.affiliation: Austrian Institute of Technology, Donau-City-Strasse 1, 1220 Vienna, Austria. manfred.hartmann@ait.ac.at
authors.forename: Manfred M
authors.initials: MM
authors.lastname: Hartmann
authors.forename: Franz
authors.initials: F
authors.lastname: Fürbass
authors.forename: Hannes
authors.initials: H
authors.lastname: Perko
authors.forename: Ana
authors.initials: A
authors.lastname: Skupch
authors.forename: Katharina
authors.initials: K
authors.lastname: Lackmayer
authors.forename: Christoph
authors.initials: C
authors.lastname: Baumgartner
authors.forename: Tilmann
authors.initials: T
authors.lastname: Kluge
mesh_terms: D000465:Algorithms; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D004569:Electroencephalography / Q000379:methods*; D006801:Humans; D009862:Online Systems; D009991:Oscillometry / Q000379:methods*; D015203:Reproducibility of Results; D012640:Seizures / Q000175:diagnosis*; D012680:Sensitivity and Specificity; D012984:Software