Discovering <scp>EEG</scp> biomarkers of Lennox–Gastaut syndrome through unsupervised time–frequency analysis
s-EPIC provides a quantitative approach to waveform identification that could be broadly applied to EEG from both healthy subjects and those with suspected pathology. s-EPIC can objectively identify and characterize relevant EEG waveforms without visual review or assumptions about the waveform's morphology and could therefore be a powerful tool for the discovery and refinement of EEG biomarkers.
Authors
- Derek Hu (ORCID: https://orcid.org/0000-0003-4720-2622)
- David J. Adams (ORCID: https://orcid.org/0000-0002-7030-2288)
- Shaun A. Hussain (ORCID: https://orcid.org/0000-0001-6947-8852)
- Beth A. Lopour (ORCID: https://orcid.org/0000-0003-4233-4802)
- Daniel W. Shrey (ORCID: https://orcid.org/0000-0002-3163-4773)
- Mandeep Rana (ORCID: https://orcid.org/0000-0001-7213-636X)
- Marco Antonio Pinto-Orellana (ORCID: https://orcid.org/0000-0001-6495-1305)
- Luat Do
Institutions
- Children's Hospital of Orange County (US)
- University of California, Los Angeles (US)
- University of California, Irvine (US)
- Irvine University (US)
- California State University, Long Beach (US)
Publication Details
- Journal
- Epilepsia
- Published
- 2024-12-12
- DOI
- https://doi.org/10.1111/epi.18211
- Citations
- 4
- Primary Topic
- EEG and Brain-Computer Interfaces
- Type
- article
- Field-Weighted Citation Impact
- 1.37
Funders
- LGS Foundation