Nonictal electroencephalographic measures for the diagnosis of functional seizures
Machine learning was much more effective than using individual features and could be a powerful aid in FS diagnosis. Furthermore, combining the frequency bands improved the accuracy of the classifiers in most cases, and the lowest performing EEG bands were consistently delta and gamma.
Authors
- Chloe Hinchliffe (ORCID: https://orcid.org/0000-0002-5002-1120)
- Hongying Tang (ORCID: https://orcid.org/0000-0002-7246-3544)
- Samia Elkommos
- Mahinda Yogarajah (ORCID: https://orcid.org/0000-0003-3509-3756)
- Daniel Abásolo (ORCID: https://orcid.org/0000-0002-4268-2885)
Institutions
- St George's, University of London (GB)
- King's College London (GB)
- University of Surrey (GB)
- Atkins (United Kingdom) (GB)
- Catalyst (GB)
- St George's Hospital (GB)
- National Hospital for Neurology and Neurosurgery (GB)
- University College London (GB)
- Epilepsy Society (GB)
- Newcastle University (GB)
Publication Details
- Journal
- Epilepsia
- Published
- 2024-09-10
- DOI
- https://doi.org/10.1111/epi.18110
- Citations
- 5
- Primary Topic
- EEG and Brain-Computer Interfaces
- Type
- article
- Field-Weighted Citation Impact
- 1.71
Funders
- Medical Research Council