title: Automated identification of ERP peaks through Dynamic Time Warping: an application to developmental dyslexia.
abstract: OBJECTIVE
  This article proposes a method to automatically identify and label event-related potential (ERP) components with high accuracy and precision.
  
  
  METHODS
  We present a framework, referred to as peak-picking Dynamic Time Warping (ppDTW), where a priori knowledge about the ERPs under investigation is used to define a reference signal. We developed a combination of peak-picking and Dynamic Time Warping (DTW) that makes the temporal intervals for peak-picking adaptive on the basis of the morphology of the data. We tested the procedure on experimental data recorded from a control group and from children diagnosed with developmental dyslexia.
  
  
  RESULTS
  We compared our results with the traditional peak-picking. We demonstrated that our method achieves better performance than peak-picking, with an overall precision, recall and F-score of 93%, 86% and 89%, respectively, versus 93%, 80% and 85% achieved by peak-picking.
  
  
  CONCLUSIONS
  We showed that our hybrid method outperforms peak-picking, when dealing with data involving several peaks of interest.
  
  
  CONCLUSIONS
  The proposed method can reliably identify and label ERP components in challenging event-related recordings, thus assisting the clinician in an objective assessment of amplitudes and latencies of peaks of clinical interest.
authors.affiliation: Ghent University, Department of Electronics and Information Systems, MEDISIP-IBBT-IbiTech, De Pintelaan 185, B-9000 Ghent, Belgium. sara.assecondi@gmail.com
authors.forename: Sara
authors.initials: S
authors.lastname: Assecondi
authors.forename: A M
authors.initials: AM
authors.lastname: Bianchi
authors.forename: H
authors.initials: H
authors.lastname: Hallez
authors.forename: S
authors.initials: S
authors.lastname: Staelens
authors.forename: S
authors.initials: S
authors.lastname: Casarotto
authors.forename: I
authors.initials: I
authors.lastname: Lemahieu
authors.forename: G A
authors.initials: GA
authors.lastname: Chiarenza
mesh_terms: D000465:Algorithms; D001921:Brain / Q000502:physiology*; D002648:Child; D004410:Dyslexia / Q000503:physiopathology*; D005071:Evoked Potentials / Q000502:physiology*; D006801:Humans; D012815:Signal Processing, Computer-Assisted