title: High Frequency Oscillations and spikes: Separating real HFOs from false oscillations.
abstract: OBJECTIVE
  To demonstrate and quantify the occurrence of false High Frequency Oscillations (HFOs) generated by the filtering of sharp events. To distinguish real HFOs from spurious ones using analysis of the raw signal.
  
  
  METHODS
  We developed a new method to prevent false HFO detections due to the filtering effect by detecting oscillations in the raw signal at the time of sharp events. We specified temporal features to classify sharp events with and without HFOs using support vector machine in both ripple and fast ripple bands. The traditionally used time-frequency representation served as the gold standard to indicate real and false HFOs.
  
  
  RESULTS
  44% of ripples and 43% of FRs concurring with sharp events were found to be false HFOs. Sharp events with HFOs had significantly more oscillations in the raw signal than sharp events without. They could be distinguished from false HFOs with accuracy of 76.6% in the ripple band and 72.6% in the fast ripple band.
  
  
  CONCLUSIONS
  It may be most appropriate to detect HFOs as oscillations not only on the filtered signal but also on the raw signal. The classical time-frequency display used for identifying HFOs should be used with great care due to the possible masking effect of broadband activities.
  
  
  CONCLUSIONS
  The separation of real HFOs from broadband activities will raise the validity of HFO detection methods and will therefore support future HFO investigations.
authors.affiliation: Montreal Neurological Institute, McGill University, Montréal, Québec, Canada. Electronic address: mina.amiri@mail.mcgill.ca.
authors.forename: Mina
authors.initials: M
authors.lastname: Amiri
authors.affiliation: École De Technologie Supérieure, Département de Génie Électrique, Montréal, Québec, Canada; Centre de Recherches Mathématiques, Montréal, Québec, Canada.
authors.forename: Jean-Marc
authors.initials: JM
authors.lastname: Lina
authors.affiliation: NEUROFARBA Department, University of Florence, Italy.
authors.forename: Francesca
authors.initials: F
authors.lastname: Pizzo
authors.affiliation: Montreal Neurological Institute, McGill University, Montréal, Québec, Canada.
authors.forename: Jean
authors.initials: J
authors.lastname: Gotman
mesh_terms: D000200:Action Potentials; D000328:Adult; D000465:Algorithms; D004569:Electroencephalography / Q000379:methods*; D004827:Epilepsy / Q000503:physiopathology*; D005260:Female; D006801:Humans; D008297:Male; D008875:Middle Aged
keywords: Epilepsy; Filtering; High Frequency Oscillations; Sharp event; Time–frequency