Machine learning for forecasting initial seizure onset in neonatal hypoxic–ischemic encephalopathy
This study demonstrates an ML model that combines QEEG with clinical features to forecast time-dependent risk of initial seizure onset in neonatal HIE. Spectral power evolution is an early EEG marker of seizure risk in neonatal HIE.
Authors
- Edilberto Amorim (ORCID: https://orcid.org/0000-0001-6972-5622)
- Marie‐Coralie Cornet (ORCID: https://orcid.org/0000-0001-5233-0800)
- Adam L. Numis (ORCID: https://orcid.org/0000-0002-1594-9812)
- Vikram R. Rao (ORCID: https://orcid.org/0000-0002-6389-2638)
- Hannah C. Glass (ORCID: https://orcid.org/0000-0002-3879-1966)
- Jonathan Kim (ORCID: https://orcid.org/0000-0001-5817-348X)
- Danilo Bernardo (ORCID: https://orcid.org/0000-0003-1077-2963)
Institutions
- University of California, San Francisco (US)
- Stanford University (US)
Publication Details
- Journal
- Epilepsia
- Published
- 2024-11-04
- DOI
- https://doi.org/10.1111/epi.18163
- Citations
- 12
- Primary Topic
- Neonatal and fetal brain pathology
- Type
- article
- Field-Weighted Citation Impact
- 13.31
Funders
- University of California
- National Institutes of Health
- National Institute of Neurological Disorders and Stroke