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Current Directions in Biomedical Engineering

Joint Journal of the German Society for Biomedical Engineering in VDE and the Austrian and Swiss Societies for Biomedical Engineering

Editor-in-Chief: Dössel, Olaf

Wissenschaftlicher Beirat: Augat, Peter / Buzug, Thorsten M. / Haueisen, Jens / Jockenhoevel, Stefan / Knaup-Gregori, Petra / Kraft, Marc / Lenarz, Thomas / Leonhardt, Steffen / Malberg, Hagen / Penzel, Thomas / Plank, Gernot / Radermacher, Klaus M. / Schkommodau, Erik / Stieglitz, Thomas / Urban, Gerald A.


CiteScore 2018: 0.47

Source Normalized Impact per Paper (SNIP) 2018: 0.377

Open Access
Online
ISSN
2364-5504
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Evaluation of an automated analysis for pain-related evoked potentials

Michael Wulf
  • Korrespondenzautor
  • Research Center for BioMedicalTechnology (BMT), University of Applied Sciences and Arts, Sonnenstr. 96, 44139 Dortmund, Germany
  • E-Mail
  • Weitere Artikel des Autors:
  • De Gruyter OnlineGoogle Scholar
/ Lynn Eitner / Thomas Felderhoff
  • Research Center for BioMedicalTechnology (BMT), University of Applied Sciences and Arts, Dortmund, Germany
  • Weitere Artikel des Autors:
  • De Gruyter OnlineGoogle Scholar
/ Özüm Özgül / Gerhard Staude / Christoph Maier / Andreas Knopp / Oliver Höffken
Online erschienen: 07.09.2017 | DOI: https://doi.org/10.1515/cdbme-2017-0087

Abstract

This paper presents initial steps towards an auto-mated analysis for pain-related evoked potentials (PREP) to achieve a higher objectivity and non-biased examination as well as a reduction in the time expended during clinical daily routines. While manually examining, each epoch of an en-semble of stimulus-locked EEG signals, elicited by electrical stimulation of predominantly intra-epidermal small nerve fibers and recorded over the central electrode (Cz), is in-spected for artifacts before calculating the PREP by averag-ing the artifact-free epochs. Afterwards, specific peak-latencies (like the P0-, N1 and P1-latency) are identified as certain extrema in the PREP’s waveform. The proposed automated analysis uses Pearson’s correlation and low-pass differentiation to perform these tasks. To evaluate the auto-mated analysis’ accuracy its results of 232 datasets were compared to the results of the manually performed examina-tion. Results of the automated artifact rejection were compa-rable to the manual examination. Detection of peak-latencies was more heterogeneous, indicating some sensitivity of the detected events upon the criteria used during data examina-tion.

Keywords: biomedical signal processing; pain-related evoked potentials

Artikelinformationen

Online erschienen: 07.09.2017


Quellenangabe: Current Directions in Biomedical Engineering, Band 3, Heft 2, Seiten 413–416, ISSN (Online) 2364-5504, DOI: https://doi.org/10.1515/cdbme-2017-0087.

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©2017 Michael Wulf et al., published by De Gruyter. This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License. BY-NC-ND 4.0

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