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BY-NC-ND 4.0 license Open Access Published by De Gruyter September 7, 2017

Evaluation of an automated analysis for pain-related evoked potentials

  • Michael Wulf EMAIL logo , Lynn Eitner , Thomas Felderhoff , Özüm Özgül , Gerhard Staude , Christoph Maier , Andreas Knopp and Oliver Höffken


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.

Published Online: 2017-09-07

©2017 Michael Wulf et al., published by De Gruyter

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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