Comparison of existing electrode designs for preferential activation of cutaneous nociceptors

Aida Hejlskov Poulsen, Jenny Tigerholm, Suzan Meijs, Ole Kaeseler Andersen, Carsten Dahl Mørch

Research output: Contribution to journalJournal articleResearchpeer-review

31 Citations (Scopus)
121 Downloads (Pure)

Abstract

OBJECTIVE: Over the recent years, several small area electrodes have been introduced as tools for preferential stimulation of small cutaneous nerve fibers. However, the performance of the electrodes is highly debated and have not previously been systematically compared. The electrodes have been developed empirically and little is known about the electrical potential they produce in the skin, and how this influences the nerve fiber activation. The objective of the present study was to develop and validate a computational model to compare the preferential stimulation of small fibers for electrodes of different designs.

APPROACH: A finite element model of the skin was developed and coupled with an Aβ-fiber and an Aδ-fiber multi-compartmental nerve fiber model, to describe the current spread and consequent nerve fiber activation produced by five different surface electrodes; intra-epidermal, planar concentric, pin, planar array, and patch. The model was validated through experimental assessments of the strength-duration relationship, impedance, and reaction times.

MAIN RESULTS: The computational model predicted the intra-epidermal electrode to be the most preferential for small fiber activation. The intra-epidermal electrode was, however, also found to be the most sensitive to positioning relative to nerve fiber location, which may limit the practical use of the electrode.

SIGNIFICANCE: The present study highlights the influence of different electrode design features on the current spread and resulting activation of cutaneous nerve fibers. Additionally, the computational model may be used for the optimization of electrode design towards even better preferential stimulation of small fibers.

Original languageEnglish
Article number036026
JournalJournal of Neural Engineering
Volume17
Issue number3
Number of pages17
ISSN1741-2560
DOIs
Publication statusPublished - 12 Jun 2020

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