Deep brain drug-delivery control using vagus nerve communications

Donohoe, Michael and Jennings, Brendan and Balasubramaniam, Sasitharan (2020) Deep brain drug-delivery control using vagus nerve communications. Computer Networks, 171. ISSN 1389-1286

Full text not available from this repository. (Request a copy)


Vagus nerve stimulation (VNS) uses electrical impulses applied at the neck in order to mitigate the effects of, for example, epileptic seizures. We propose using VNS to provide data pulses to communicate with a drug-delivery system embedded near the brainstem. We model the generation of a vagus nerve compound action potential (CAP), calculating the signal attenuation and the resulting transmission range. The metabolic cost of CAP transmission in terms of the use of adenosine triphosphate (ATP) is also calculated. The channel capacity for on-off keying (OOK) is computed from the CAP characteristics, the neural refractory period and the level of background neural noise. The resulting low bit-rate, unidirectional asynchronous transmission system is analysed for the use of different methods of forward error correction (FEC) to improve bit-error rate (BER). We show a proposed data packet structure that could deliver instructions to an embedded drug-delivery system with multiple addressable drug reservoirs. We also analyse the scope for powering the drug-delivery system with energy harvested from cerebrospinal glucose.

Item Type: Article
Additional Information: Funding Information: This work is supported by the Academy of Finland FiDiPro programme for the project “Nanocommunications Networks” 2012 - 2016, and the Finnish Academy Research Fellow programme under Project no. 284531. It is also partly funded by the Irish Higher Education Authority under the Programme for Research in Third Level Institutions (PRTLI) cycle 5, which is co-funded by the European Regional Development Fund (ERDF), via the Telecommunications Graduate Initiative, and by Science Foundation Ireland via the CONNECT research centre (grant no. 13/RC/2077 ). Publisher Copyright: © 2020 Elsevier B.V.
Uncontrolled Keywords: /dk/atira/pure/subjectarea/asjc/1700/1705
Departments or Groups:
Depositing User: Admin SSL
Date Deposited: 19 Oct 2022 23:10
Last Modified: 15 Jul 2023 19:45

Actions (login required)

View Item View Item