title: Interaction with slow waves during sleep improves discrimination of physiologic and pathologic high-frequency oscillations (80-500 Hz).
abstract: To characterize the interaction between physiologic and pathologic high-frequency oscillations (HFOs) and slow waves during sleep, and to evaluate the practical significance of these interactions by automatically classifying channels as recording from normal or epileptic brain regions.
  
  
  
  We automatically detected HFOs in intracerebral electroencephalography (EEG) recordings of 45 patients. We characterized the interaction between the HFOs and the amplitude and phase of automatically detected slow waves during sleep. We computed features associated with HFOs, and compared classic features such as rate, amplitude, duration, and frequency to novel features related to the interaction between HFOs and slow waves. To quantify the practical significance of the difference in these features we classified the channels as recording from normal/epileptic regions using logistic regression. We assessed the results in different brain regions to study differences in the HFO characteristics at the lobar level.
  
  
  
  We found a clear difference in the coupling between the phase of slow waves during sleep and the occurrence of HFOs. In channels recording physiologic activity, the HFOs tend to occur after the peak of the deactivated state of the slow wave, and in channels with epileptic activity, the HFOs occur more often before this peak. This holds for HFOs in the ripple (80-250 Hz) and fast ripple (250-500 Hz) bands, and different regions of the brain. When using this interaction to automatically classify channels as recording from normal/epileptic brain regions, the performance is better than when using other HFO characteristics. We confirmed differences in the HFO characteristics in mesiotemporal structures and in the occipital lobe.
  
  
  
  We found the association between slow waves and HFOs to be different in normal and epileptic brain regions, emphasizing their different origin. This is also of practical significance, since it improves the separation between channels recording from normal and epileptic brain regions.
authors.affiliation: Montreal Neurological Institute and Hospital, McGill University, Montreal, Québec, Canada.|LEICI, CONICET-National University of La Plata, La Plata, Argentina.
authors.forename: Nicolás
authors.initials: N
authors.lastname: von Ellenrieder
authors.affiliation: Montreal Neurological Institute and Hospital, McGill University, Montreal, Québec, Canada.
authors.forename: Birgit
authors.initials: B
authors.lastname: Frauscher
authors.affiliation: Montreal Neurological Institute and Hospital, McGill University, Montreal, Québec, Canada.
authors.forename: François
authors.initials: F
authors.lastname: Dubeau
authors.affiliation: Montreal Neurological Institute and Hospital, McGill University, Montreal, Québec, Canada.
authors.forename: Jean
authors.initials: J
authors.lastname: Gotman
mesh_terms: D001683:Biological Clocks / Q000502:physiology*; D058256:Brain Waves / Q000502:physiology*; D004569:Electroencephalography; D004827:Epilepsy / Q000473:pathology* / Q000503:physiopathology*; D005260:Female; D006801:Humans; D008297:Male; D012372:ROC Curve; D012189:Retrospective Studies; D012890:Sleep / Q000502:physiology*
keywords: Automatic detection; Classification; Depth EEG; HFO; NREM sleep