Automated identification of ERP peaks through Dynamic Time Warping: an application to developmental dyslexia.
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.
Assecondi
Sara
S
Ghent University, Department of Electronics and Information Systems, MEDISIP-IBBT-IbiTech, De Pintelaan 185, B-9000 Ghent, Belgium. sara.assecondi@gmail.com
Bianchi
A M
AM
Hallez
H
H
Staelens
S
S
Casarotto
S
S
Lemahieu
I
I
Chiarenza
G A
GA
D000465:Algorithms; D001921:Brain / Q000502:physiology*; D002648:Child; D004410:Dyslexia / Q000503:physiopathology*; D005071:Evoked Potentials / Q000502:physiology*; D006801:Humans; D012815:Signal Processing, Computer-Assisted