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Yoo PB, Durand DM. The recording properties of a multi-contact nerve electrode as predicted by a finite element model of the canine hypoglossal nerve. Conf Proc IEEE Eng Med Biol Soc. 2004;2004:4310-3doi: 10.1109/IEMBS.2004.1404200.
Yoo, P. B., & Durand, D. M. (2004). The recording properties of a multi-contact nerve electrode as predicted by a finite element model of the canine hypoglossal nerve. Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference, 20044310-3. https://doi.org/10.1109/IEMBS.2004.1404200
Yoo, P B, and Durand, D M. "The recording properties of a multi-contact nerve electrode as predicted by a finite element model of the canine hypoglossal nerve." Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference vol. 2004 (2004): 4310-3. doi: https://doi.org/10.1109/IEMBS.2004.1404200
Yoo PB, Durand DM. The recording properties of a multi-contact nerve electrode as predicted by a finite element model of the canine hypoglossal nerve. Conf Proc IEEE Eng Med Biol Soc. 2004;2004:4310-3. doi: 10.1109/IEMBS.2004.1404200. PMID: 17271258.
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Conf Proc IEEE Eng Med Biol Soc. 2004;2004:4310-3. doi: 10.1109/IEMBS.2004.1404200.

The recording properties of a multi-contact nerve electrode as predicted by a finite element model of the canine hypoglossal nerve.

Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference

P B Yoo, D M Durand

Affiliations

  1. Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, USA.

PMID: 17271258 DOI: 10.1109/IEMBS.2004.1404200

Abstract

Most functional electrical stimulation (FES) systems rely only on unidirectional (i.e., efferent) activation of the target organ to yield therapeutic outcomes. For applications involving multi-fasciculated nerves, however, artificial sensors have exhibited limited results. As such, the flat-interface-nerve-electrode (FINE) is presented as a means of obtaining an effective closed-loop control system. To investigate the ability of this electrode to achieve selective recordings at physiological signal-to-noise ratio (SNR), a finite element model (JFEM) of a beagle hypoglossal nerve with an implanted FINE was constructed. Action potentials (AP) were generated at various SNR levels and the performance of the electrode was assessed with a selectivity index (0 < or = SI < or = 1; ability of the electrode to distinguish two active sources). Computer simulations yielded a selective range (0.05 < or = SI < or = 0.76) that was (1) related to the inter-fiber distance and (2) used to predict the minimum inter-fiber distance (0.23 mm < or = d < or = 1.42 mm) required for selective recording. The results of this study suggest that the FINE can record neural activity from a multi-fasciculated nerve and, more importantly, distinguish neural activity from pairs of fascicles at physiologic SNR.

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