title: Automatic detection of fast ripples.
abstract: OBJECTIVE
  We propose a new method for automatic detection of fast ripples (FRs) which have been identified as a potential biomarker of epileptogenic processes.
  
  
  METHODS
  This method is based on a two-stage procedure: (i) global detection of events of interest (EOIs, defined as transient signals accompanied with an energy increase in the frequency band of interest 250-600Hz) and (ii) local energy vs. frequency analysis of detected EOIs for classification as FRs, interictal epileptic spikes or artifacts. For this second stage, two variants were implemented based either on Fourier or wavelet transform. The method was evaluated on simulated and real depth-EEG signals (human, animal). The performance criterion was based on receiving operator characteristics.
  
  
  RESULTS
  The proposed detector showed high performance in terms of sensitivity and specificity.
  
  
  CONCLUSIONS
  As designed to specifically detect FRs, the method outperforms any method simply based on the detection of energy changes in high-pass filtered signals and avoids spurious detections caused by sharp transient events often present in raw signals.
  
  
  CONCLUSIONS
  In most of epilepsy surgery units, huge data sets are generated during pre-surgical evaluation. We think that the proposed detection method can dramatically decrease the workload in assessing the presence of FRs in intracranial EEGs.
authors.affiliation: INSERM, U1099, Rennes F-35000, France.
authors.forename: Gwénaël
authors.initials: G
authors.lastname: Birot
authors.forename: Amar
authors.initials: A
authors.lastname: Kachenoura
authors.forename: Laurent
authors.initials: L
authors.lastname: Albera
authors.forename: Christian
authors.initials: C
authors.lastname: Bénar
authors.forename: Fabrice
authors.initials: F
authors.lastname: Wendling
mesh_terms: D000818:Animals; D001921:Brain / Q000502:physiology*; D004569:Electroencephalography / Q000379:methods*; D006801:Humans; D012680:Sensitivity and Specificity; D012815:Signal Processing, Computer-Assisted