title: Channel selection for automatic seizure detection.
abstract: OBJECTIVE
  To investigate the performance of epileptic seizure detection using only a few of the recorded EEG channels and the ability of software to select these channels compared with a neurophysiologist.
  
  
  METHODS
  Fifty-nine seizures and 1419 h of interictal EEG are used for training and testing of an automatic channel selection method. The characteristics of the seizures are extracted by the use of a wavelet analysis and classified by a support vector machine. The best channel selection method is based upon maximum variance during the seizure.
  
  
  RESULTS
  Using only three channels, a seizure detection sensitivity of 96% and a false detection rate of 0.14/h were obtained. This corresponds to the performance obtained when channels are selected through visual inspection by a clinical neurophysiologist, and constitutes a 4% improvement in sensitivity compared to seizure detection using channels recorded directly on the epileptic focus.
  
  
  CONCLUSIONS
  Based on our dataset, automatic seizure detection can be done using only three EEG channels without loss of performance. These channels should be selected based on maximum variance and not, as often done, using the focal channels.
  
  
  CONCLUSIONS
  With this simple automatic channel selection method, we have shown a computational efficient way of making automatic seizure detection.
authors.affiliation: Technical University of Denmark, Department of Electrical Engineering, Building 349, Oersteds Plads, 2800 Kgs. Lyngby, Denmark. jhe@elektro.dtu.dk
authors.forename: Jonas
authors.initials: J
authors.lastname: Duun-Henriksen
authors.forename: Troels Wesenberg
authors.initials: TW
authors.lastname: Kjaer
authors.forename: Rasmus Elsborg
authors.initials: RE
authors.lastname: Madsen
authors.forename: Line Sofie
authors.initials: LS
authors.lastname: Remvig
authors.forename: Carsten Eckhart
authors.initials: CE
authors.lastname: Thomsen
authors.forename: Helge Bjarup Dissing
authors.initials: HB
authors.lastname: Sorensen
mesh_terms: D000465:Algorithms; D003936:Diagnosis, Computer-Assisted / Q000379:methods; D004569:Electroencephalography / Q000295:instrumentation / Q000379:methods; D004827:Epilepsy / Q000175:diagnosis*; D006801:Humans; D012680:Sensitivity and Specificity; D060388:Support Vector Machine