Machine learning for forecasting initial seizure onset in neonatal hypoxic-ischemic encephalopathy.

ABSTRACT:

Objective

This study was undertaken to develop a machine learning (ML) model to forecast initial seizure onset in neonatal hypoxic-ischemic encephalopathy (HIE) utilizing clinical and quantitative electroencephalogram (QEEG) features.

Methods

We developed a gradient boosting ML model (Neo-GB) that utilizes clinical features and QEEG to forecast time-dependent seizure risk. Clinical variables included cord blood gas values, Apgar scores, gestational age at birth, postmenstrual age (PMA), postnatal age, and birth weight. QEEG features included statistical moments, spectral power, and recurrence quantification analysis (RQA) features. We trained and evaluated Neo-GB on a University of California, San Francisco (UCSF) neonatal HIE dataset, augmenting training with publicly avail

SUBMITTER: Bernardo D 

PROVIDER: S-EPMC11742638 | biostudies-literature | 2025 Jan

REPOSITORIES: biostudies-literature