Title
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Automated identification of ERP peaks through Dynamic Time Warping : an application to developmental dyslexia
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Author
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Abstract
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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. Conclusion We showed that our hybrid method outperforms peak-picking, when dealing with data involving several peaks of interest. Significance 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. |
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Language
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English
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Source (journal)
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Clinical neurophysiology. - Amsterdam
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Publication
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Amsterdam
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2009
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ISSN
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1388-2457
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DOI
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10.1016/J.CLINPH.2009.06.023
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Volume/pages
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120
:10
(2009)
, p. 1819-1827
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ISI
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000272018900011
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Full text (Publisher's DOI)
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Full text (publisher's version - intranet only)
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