title: Evaluation of the AFOP/DAFOP method for automatic filtering of EEGs of patients with epilepsy.
abstract: OBJECTIVE
  Further developments in EEG monitoring necessitate new methods of filtering to eliminate artifacts, without transforming relevant signals. This article presents an automatic filtering of EEG recordings, based on a spatio-temporal method called Adaptive Filtering by Optimal Projection or Dual Adaptive Filtering by Optimal Projection. Evaluation of filtering methods is difficult, and comparisons between methods remain a challenge; here, we present a method to score the visual assessment of the EEG. The aim of this study was to evaluate an automatic filtering method, called Adaptive Filtering by Optimal Projection, improved by Dual Adaptive Filtering by Optimal Projection, of EEG recordings of patients with epilepsy.
  
  
  METHODS
  Two hundred forty-eight nonfiltered EEG segments of 20 seconds each were selected from 35 EEG recordings of 27 different patients by 3 clinical neurophysiologists based on their content. The reading quality as well as the proportions of artifacts and of cerebral activity removed after filtering were evaluated on a scale of 0 to 4. The mean square difference of amplitude before and after filtering was computed in specific spectral band.
  
  
  RESULTS
  The artifacts were largely removed (82% for muscular, 72% for ocular, and 71% for electrode artifacts). The readability was improved on an average by two points for pages containing epileptic seizures, and by one point for those containing alpha rhythms, slow waves, and spikes. After filtering, consistency tests showed a consensus (Spearman correlation [0.69-0.79]) on the removal of the artifact versus loss of information. The spectral analysis showed equivalent results (0.16% mean square difference in the alpha band).
  
  
  CONCLUSIONS
  Our filtering method is effective in removing artifacts without altering relevant signals. The significance is that we evaluated a new automated method of filtering EEG that is easy to use for both for the analysis of routine EEG and in the field of epilepsy at large.
authors.affiliation: *Hautes Etudes d'Ingénieur Unité de Traitement des Signaux Biomédicaux, Lille, France; †Groupe Hospitalier de l'Université Catholique de Lille, Lomme Cedex, France; ‡Université Catholique de Lille, Lille, France; §Service des Explorations Fonctionnelles; ‖Faculté Libre de Médecine de Lille, Lille, France; and ¶Service de Neurophysiologie Clinique, Hôpital Salengro, Lille, France.
authors.forename: Laurent
authors.initials: L
authors.lastname: Peyrodie
authors.forename: Philippe
authors.initials: P
authors.lastname: Gallois
authors.forename: Samuel
authors.initials: S
authors.lastname: Boudet
authors.forename: Hua
authors.initials: H
authors.lastname: Cao
authors.forename: Pascal
authors.initials: P
authors.lastname: Barbaste
authors.forename: William
authors.initials: W
authors.lastname: Szurhaj
mesh_terms: D000222:Adaptation, Physiological; D000293:Adolescent; D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D016477:Artifacts; D058256:Brain Waves / Q000502:physiology*; D004569:Electroencephalography / Q000379:methods*; D004827:Epilepsy / Q000503:physiopathology*; D005260:Female; D006801:Humans; D008297:Male; D008875:Middle Aged; D012815:Signal Processing, Computer-Assisted; D055815:Young Adult