Peripheral nerve decoding algorithms for bioelectronic medicines

Supervisors

Simon Schultz (Bioengineering)
Nick Jones (Maths)
Victor Pikov (GSK)

Bioelectronic medicine, in which devices connected to groups of individual nerve fibres are used to control the patterns of electrical signals to restore health to organs and biological functions, has been suggested to have the potential to make major advances in the treatment of conditions resistant to drugs, including diabetes, obesity, hypertension and pulmonary diseases (Famm et al, Nature 496:159-61, 2013).

The development of bioelectronic medicines, however, is contingent upon the existence of suitable technology for monitoring and perturbing activity in peripheral nerve fibres; in particular, being able to “read out” and interpret signals carried by a peripheral nerve fibre is an essential milestone.

In this project, we will develop decoding algorithms capable of reading out both continuous physiological signals, and discrete “events”, from peripheral nervous system (PNS) electrical signals. These algorithms will be applied to a variety of datasets collected by members of a research network in Bioelectronic Medicines that has been established by GlaxoSmithKline, plc.

The project will involve two phases. The first year will comprise an MRes Project, in which the student will gain a deep understanding of the different approaches that can be taken to decoding physiological signals, testing algorithms on simulated data, which will be generated in the course of the project; we expect this computational model of a peripheral nerve to be a major output of the MRes year. In the following years, and exploiting and advancing a new signal processing architecture, the student will develop refined decoding algorithms optimised for use with peripheral nerve signals at several spatial scales, and will work with research groups across the GSK network to apply these algorithms to real PNS datasets.

Student

Carl Lubba