title: Automatic detection of prominent interictal spikes in intracranial EEG: validation of an algorithm and relationsip to the seizure onset zone.
abstract: OBJECTIVE
  To develop an algorithm for the automatic quantitative description and detection of spikes in the intracranial EEG and quantify the relationship between prominent spikes and the seizure onset zone.
  
  
  METHODS
  An algorithm was developed for the quantification of time-frequency properties of spikes (upslope, instantaneous energy, downslope) and their statistical representation in a univariate generalized extreme value distribution. Its performance was evaluated in comparison to expert detection of spikes in intracranial EEG recordings from 10 patients. It was subsequently used in 18 patients to detect prominent spikes and quantify their spatial relationship to the seizure onset area.
  
  
  RESULTS
  The algorithm displayed an average sensitivity of 63.4% with a false detection rate of 3.2 per minute for the detection of individual spikes and an average sensitivity of 88.6% with a false detection rate of 1.4% for the detection of intracranial EEG contacts containing the most prominent spikes. Prominent spikes occurred closer to the seizure onset area than less prominent spikes but they overlapped with it only in a minority of cases (3/18).
  
  
  CONCLUSIONS
  Automatic detection and quantification of the morphology of spikes increases their utility to localize the seizure onset area. Prominent spikes tend to originate mostly from contacts located in the close vicinity of the seizure onset area rather than from within it.
  
  
  CONCLUSIONS
  Quantitative analysis of time-frequency characteristics and spatial distribution of intracranial spikes provides complementary information that may be useful for the localization of the seizure-onset zone.
authors.affiliation: Comprehensive Epilepsy Center and Computational Neurophysiology Laboratory, Dept. of Neurology, School of Medicine, Yale University, Yale-New Haven Hospital, New Haven, CT, USA. Electronic address: nicolas.gaspard@yale.edu.
authors.forename: Nicolas
authors.initials: N
authors.lastname: Gaspard
authors.affiliation: Comprehensive Epilepsy Center and Computational Neurophysiology Laboratory, Dept. of Neurology, School of Medicine, Yale University, Yale-New Haven Hospital, New Haven, CT, USA.
authors.forename: Rafeed
authors.initials: R
authors.lastname: Alkawadri
authors.affiliation: Comprehensive Epilepsy Center and Computational Neurophysiology Laboratory, Dept. of Neurology, School of Medicine, Yale University, Yale-New Haven Hospital, New Haven, CT, USA.
authors.forename: Pue
authors.initials: P
authors.lastname: Farooque
authors.affiliation: Comprehensive Epilepsy Center and Computational Neurophysiology Laboratory, Dept. of Neurology, School of Medicine, Yale University, Yale-New Haven Hospital, New Haven, CT, USA.
authors.forename: Irina I
authors.initials: II
authors.lastname: Goncharova
authors.affiliation: Comprehensive Epilepsy Center and Computational Neurophysiology Laboratory, Dept. of Neurology, School of Medicine, Yale University, Yale-New Haven Hospital, New Haven, CT, USA.
authors.forename: Hitten P
authors.initials: HP
authors.lastname: Zaveri
mesh_terms: D000328:Adult; D000465:Algorithms; D004569:Electroencephalography / Q000145:classification*; D004867:Equipment Design; D005189:False Positive Reactions; D006801:Humans; D012640:Seizures / Q000175:diagnosis*; D012680:Sensitivity and Specificity; D012815:Signal Processing, Computer-Assisted / Q000295:instrumentation*
keywords: Automatic detection; General extreme value statistics; Interictal spike detection; Intracranial EEG; Localization of the seizure onset zone; Mahalanobis distance