title: Seizure detection with automated EEG analysis: a validation study focusing on periodic patterns.
abstract: OBJECTIVE
  To evaluate an automated seizure detection (ASD) algorithm in EEGs with periodic and other challenging patterns.
  
  
  METHODS
  Selected EEGs recorded in patients over 1year old were classified into four groups: A. Periodic lateralized epileptiform discharges (PLEDs) with intermixed electrical seizures. B. PLEDs without seizures. C. Electrical seizures and no PLEDs. D. No PLEDs or seizures. Recordings were analyzed by the Persyst P12 software, and compared to the raw EEG, interpreted by two experienced neurophysiologists; Positive percent agreement (PPA) and false-positive rates/hour (FPR) were calculated.
  
  
  RESULTS
  We assessed 98 recordings (Group A=21 patients; B=29, C=17, D=31). Total duration was 82.7h (median: 1h); containing 268 seizures. The software detected 204 (=76.1%) seizures; all ictal events were captured in 29/38 (76.3%) patients; in only in 3 (7.7%) no seizures were detected. Median PPA was 100% (range 0-100; interquartile range 50-100), and the median FPR 0/h (range 0-75.8; interquartile range 0-4.5); however, lower performances were seen in the groups containing periodic discharges.
  
  
  CONCLUSIONS
  This analysis provides data regarding the yield of the ASD in a particularly difficult subset of EEG recordings, showing that periodic discharges may bias the results.
  
  
  CONCLUSIONS
  Ongoing refinements in this technique might enhance its utility and lead to a more extensive application.
authors.affiliation: Department of Clinical Neurosciences, Centre Hospitalier Universitaire Vaudois (CHUV), and University of Lausanne, Lausanne, Switzerland.
authors.forename: Alba
authors.initials: A
authors.lastname: Sierra-Marcos
authors.affiliation: Persyst Development Corporation, San Diego, CA, USA.
authors.forename: Mark L
authors.initials: ML
authors.lastname: Scheuer
authors.affiliation: Department of Clinical Neurosciences, Centre Hospitalier Universitaire Vaudois (CHUV), and University of Lausanne, Lausanne, Switzerland. Electronic address: andrea.rossetti@chuv.ch.
authors.forename: Andrea O
authors.initials: AO
authors.lastname: Rossetti
mesh_terms: D000293:Adolescent; D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D000465:Algorithms; D002540:Cerebral Cortex / Q000503:physiopathology*; D002648:Child; D002675:Child, Preschool; D004569:Electroencephalography / Q000379:methods*; D005260:Female; D006801:Humans; D008297:Male; D008875:Middle Aged; D012640:Seizures / Q000175:diagnosis* / Q000503:physiopathology; D012984:Software; D055815:Young Adult
keywords: Electroencephalogram; Ictal; LPDs; PLEDs; Persyst; Seizure