title: Rapid annotation of interictal epileptiform discharges via template matching under Dynamic Time Warping.
abstract: EEG interpretation relies on experts who are in short supply. There is a great need for automated pattern recognition systems to assist with interpretation. However, attempts to develop such systems have been limited by insufficient expert-annotated data. To address these issues, we developed a system named NeuroBrowser for EEG review and rapid waveform annotation.
  
  
  
  At the core of NeuroBrowser lies on ultrafast template matching under Dynamic Time Warping, which substantially accelerates the task of annotation.
  
  
  
  Our results demonstrate that NeuroBrowser can reduce the time required for annotation of interictal epileptiform discharges by EEG experts by 20-90%, with an average of approximately 70%.
  
  
  
  In comparison with conventional manual EEG annotation, NeuroBrowser is able to save EEG experts approximately 70% on average of the time spent in annotating interictal epileptiform discharges. We have already extracted 19,000+ interictal epileptiform discharges from 100 patient EEG recordings. To our knowledge this represents the largest annotated database of interictal epileptiform discharges in existence.
  
  
  
  NeuroBrowser is an integrated system for rapid waveform annotation. While the algorithm is currently tailored to annotation of interictal epileptiform discharges in scalp EEG recordings, the concepts can be easily generalized to other waveforms and signal types.
authors.affiliation: School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore. Electronic address: jingjin@ntu.edu.sg.
authors.forename: J
authors.initials: J
authors.lastname: Jing
authors.affiliation: School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore. Electronic address: justin@dauwels.com.
authors.forename: J
authors.initials: J
authors.lastname: Dauwels
authors.affiliation: Department of Computer Engineering, Kasetsart University, Thailand. Electronic address: thanawin.r@ku.ac.th.
authors.forename: T
authors.initials: T
authors.lastname: Rakthanmanon
authors.affiliation: Department of Computer Science and Engineering, University of California, Riverside, CA, USA. Electronic address: eamonn@cs.ucr.edu.
authors.forename: E
authors.initials: E
authors.lastname: Keogh
authors.affiliation: Department of Neurology, Massachusetts General Hospital, Harvard Medical School, MA, USA. Electronic address: scash@mgh.harvard.edu.
authors.forename: S S
authors.initials: SS
authors.lastname: Cash
authors.affiliation: Department of Neurology, Massachusetts General Hospital, Harvard Medical School, MA, USA. Electronic address: mwestover@mgh.harvard.edu.
authors.forename: M B
authors.initials: MB
authors.lastname: Westover
mesh_terms: D000465:Algorithms; D001931:Brain Mapping / Q000295:instrumentation* / Q000379:methods*; D058256:Brain Waves / Q000502:physiology*; D004569:Electroencephalography / Q000379:methods*; D004827:Epilepsy / Q000503:physiopathology*; D005260:Female; D006801:Humans; D008297:Male; D017711:Nonlinear Dynamics; D012815:Signal Processing, Computer-Assisted; D012984:Software; D013997:Time Factors
keywords: Dynamic Time Warping; EEG; Graphical user interface; Interictal discharges; Rapid annotation; Spikes; Template matching