title: Fast evaluation of interictal spikes in long-term EEG by hyper-clustering.
abstract: OBJECTIVE
  The burden of reviewing long-term scalp electroencephalography (EEG) is not much alleviated by automated spike detection if thousands of events need to be inspected and mentally classified by the reviewer. This study investigated a novel technique of clustering and 24-h hyper-clustering on top of automated detection to assess whether fast review of focal interictal spike types was feasible and comparable to the spikes types observed during routine EEG review in epilepsy monitoring.
  
  
  METHODS
  Spike detection used a transformation of scalp EEG into 29 regional source activities and adaptive thresholds to increase sensitivity. Our rule-based algorithm estimated 18 parameters around each detected peak and combined multichannel detections into one event. Similarity measures were derived from equivalent location, scalp topography, and source waveform of each event to form clusters over 2-h epochs using a density-based algorithm. Similar measures were applied to all 2-h clusters to form 24-h hyper-clusters. Independent raters evaluated electroencephalography data of 50 patients with epilepsy (25 children) using traditional visual spike review and optimized hyper-cluster inspection. Congruence between visual spike types and epileptiform hyper-clusters was assessed on a sublobar level using three-dimensional (3D) peak topographies.
  
  
  RESULTS
  Visual rating found 126 different epileptiform spike types (2.5 per patient). Independently, 129 hyper-clusters were classified as epileptiform and originating in separate sublobar regions (2.6 per patient). Ninety-one percent of visual spike types matched with hyper-clusters (temporal lobe spikes 94%, extratemporal 89%). Conversely, 11% of hyper-clusters rated epileptiform had no corresponding visual spike type. Numbers were comparable in adults and children. On average, 15 hyper-clusters had to be inspected and rated per patient with an evaluation time of around 5 min.
  
  
  CONCLUSIONS
  Hyper-clustering over 24 h provides an independent tool for rapid daily evaluation of interictal spikes in long-term video-EEG monitoring. If used in addition to routine review of 2-5 min EEG per hour, sensitivity and reliability in noninvasive diagnosis of focal epilepsy increases.
authors.affiliation: BESA GmbH, Gräfelfing, Germany. mscherg@besa.de
authors.forename: Michael
authors.initials: M
authors.lastname: Scherg
authors.forename: Nicole
authors.initials: N
authors.lastname: Ille
authors.forename: Dieter
authors.initials: D
authors.lastname: Weckesser
authors.forename: Arndt
authors.initials: A
authors.lastname: Ebert
authors.forename: Andrea
authors.initials: A
authors.lastname: Ostendorf
authors.forename: Tobias
authors.initials: T
authors.lastname: Boppel
authors.forename: Susanne
authors.initials: S
authors.lastname: Schubert
authors.forename: Pål G
authors.initials: PG
authors.lastname: Larsson
authors.forename: Oliver
authors.initials: O
authors.lastname: Henning
authors.forename: Thomas
authors.initials: T
authors.lastname: Bast
mesh_terms: D000293:Adolescent; D000328:Adult; D000368:Aged; D001931:Brain Mapping; D058256:Brain Waves / Q000502:physiology*; D002648:Child; D002675:Child, Preschool; D016000:Cluster Analysis; D004569:Electroencephalography; D004827:Epilepsy / Q000175:diagnosis / Q000503:physiopathology*; D006801:Humans; D008137:Longitudinal Studies; D008875:Middle Aged; D008991:Monitoring, Physiologic; D012815:Signal Processing, Computer-Assisted; D055815:Young Adult