title: A semi-automated method for rapid detection of ripple events on interictal voltage discharges in the scalp electroencephalogram.
abstract: High frequency oscillations are emerging as a clinically important indicator of epileptic networks. However, manual detection of these high frequency oscillations is difficult, time consuming, and subjective, especially in the scalp EEG, thus hindering further clinical exploration and application. Semi-automated detection methods augment manual detection by reducing inspection to a subset of time intervals. We propose a new method to detect high frequency oscillations that co-occur with interictal epileptiform discharges.
  
  
  
  The new method proceeds in two steps. The first step identifies candidate time intervals during which high frequency activity is increased. The second step computes a set of seven features for each candidate interval. These features require that the candidate event contain a high frequency oscillation approximately sinusoidal in shape, with at least three cycles, that co-occurs with a large amplitude discharge. Candidate events that satisfy these features are stored for validation through visual analysis.
  
  
  
  We evaluate the detector performance in simulation and on ten examples of scalp EEG data, and show that the proposed method successfully detects spike-ripple events, with high positive predictive value, low false positive rate, and high intra-rater reliability.
  
  
  
  The proposed method is less sensitive than the existing method of visual inspection, but much faster and much more reliable.
  
  
  
  Accurate and rapid detection of high frequency activity increases the clinical viability of this rhythmic biomarker of epilepsy. The proposed spike-ripple detector rapidly identifies candidate spike-ripple events, thus making clinical analysis of prolonged, multielectrode scalp EEG recordings tractable.
authors.affiliation: Department of Neurology, Massachusetts General Hospital, Boston, MA 02114, United States. Electronic address: cjchu@mgh.harvard.edu.
authors.forename: Catherine J
authors.initials: CJ
authors.lastname: Chu
authors.affiliation: Voci Technologies, Inc. 6301 Forbes Ave. #120, Pittsburg, PA, 15217, United States.
authors.forename: Arthur
authors.initials: A
authors.lastname: Chan
authors.affiliation: Department of Neurology, Massachusetts General Hospital, Boston, MA 02114, United States.
authors.forename: Dan
authors.initials: D
authors.lastname: Song
authors.affiliation: Department of Neurology, Massachusetts General Hospital, Boston, MA 02114, United States.
authors.forename: Kevin J
authors.initials: KJ
authors.lastname: Staley
authors.affiliation: Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Boston, MA 02114, United States; Harvard-MIT Program in Health Sciences and Technology, Institute for Medical Engineering and Science, Massachusetts Institute of Technology, Cambridge, MA 02139, United States.
authors.forename: Steven M
authors.initials: SM
authors.lastname: Stufflebeam
authors.affiliation: Department of Mathematics and Statistics, Boston University, Boston, MA 02215, United States.
authors.forename: Mark A
authors.initials: MA
authors.lastname: Kramer
mesh_terms: D000293:Adolescent; D000818:Animals; D001921:Brain / Q000503:physiopathology*; D058256:Brain Waves / Q000502:physiology*; D002648:Child; D003198:Computer Simulation; D004569:Electroencephalography; D004827:Epilepsy / Q000175:diagnosis / Q000503:physiopathology*; D005260:Female; D006801:Humans; D008297:Male; D008959:Models, Neurological
keywords: Automated detection; EEG; High frequency oscillations; Ripples