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Machine learning and clinical neurophysiology

journal contribution
posted on 2023-09-04, 10:49 authored by Julian Ray, Lokesh Wijesekera, Silvia Cirstea
Clinical neurophysiology constructs a wealth of dynamic information pertaining to the integrity and function of both central and peripheral nervous systems. As with many technological fields, there has been an explosion of data in neurophysiology over recent years, and this requires considerable analysis by experts. Computational algorithms and especially advances in machine learning (ML) have the ability to assist with this task and potentially reveal hidden insights. In this update article, we will provide a brief overview where such technology is being applied in clinical neurophysiology and possible future directions.

History

Refereed

  • Yes

Publication title

Journal of Neurology

ISSN

1432-1459

Publisher

Springer Science and Business Media LLC

File version

  • Accepted version

Language

  • eng

Legacy posted date

2022-10-25

Legacy creation date

2022-10-25

Legacy Faculty/School/Department

Faculty of Science & Engineering

Note

This version of the article has been accepted for publication, after peer review (when applicable) and is subject to Springer Nature’s AM terms of use, but is not the Version of Record and does not reflect post-acceptance improvements, or any corrections. The Version of Record is available online at: https://doi.org/10.1007/s00415-022-11283-9

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