Stimulation Pattern Efficiency in Percutaneous Auricular Vagus Nerve Stimulation: Experimental versus Numerical data.

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Stimulation Pattern Efficiency in Percutaneous Auricular Vagus Nerve Stimulation: Experimental versus Numerical data.

IEEE Trans Biomed Eng. 2019 Oct 31;:

Authors: Kaniusas E, Samoudi AM, Kampusch S, Bald K, Tanghe E, Martens L, Joseph W, Szeles JC

Abstract
OBJECTIVE: Percutaneous electrical stimulation of the auricular vagus nerve (pVNS) is an electroceutical technology. The selection of stimulation patterns is empirical, which may lead to under-stimulation or over-stimulation. The objective is to assess the efficiency of different stimulation patterns with respect to individual perception and to compare it with numerical data based on in-silico ear models.
METHODS: Monophasic (MS), biphasic (BS) and triphasic stimulation (TS) patterns were tested in volunteers. Different clinically-relevant perception levels were assessed. In-silico models of the human ear were created with embedded fibers and vessels to assess different excitation levels.
RESULTS: TS indicates experimental superiority over BS which is superior to MS while reaching different perception levels. TS requires about 57% and 35% of BS and MS magnitude, respectively, to reach the comfortable perception. Experimental thresholds are decreased from bursted to non-bursted stimulation. Numerical results indicate a slight superiority of BS and TS over MS while reaching different excitation levels, whereas the burst length has no influence. TS yields the highest number of asynchronous action impulses per stimulation symbol for the used tripolar electrode set-up.
CONCLUSION: The comparison of experimental and numerical data favors the novel TS pattern. The analysis separates excitatory pVNS effects in the auricular periphery, as accounted by in-silico data, from the combination of peripheral and central pVNS effects in the brain, as accounted by experimental data.
SIGNIFICANCE: The proposed approach moves from an empirical selection of stimulation patterns towards efficient and optimized pVNS settings.

PMID: 31675313 [PubMed – as supplied by publisher]

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