Effective classification for neonatal brain injury using EEG feature selection based on elastic net regression and improved crow search algorithm.

ABSTRACT: Neonatal brain injury carries the risk of neurological sequelae such as epileptic seizures, cerebral palsy, intellectual disability, and even death. Classification methods based on electroencephalography (EEG) signals and machine learning algorithms are crucial for assessing neonatal brain injury. However, classification methods that utilise all features from the original EEG signals may result in lengthy training and classification times, thereby reducing the performance of the classification system. This article presents a novel classification system with a proposed feature selection method for assessing neonatal brain injury, in which the feature selection method is combined using elastic net (EN) regression and an improved crow search algorithm (ICSA), named EN-ICSA. In the EN-ICSA met

SUBMITTER: Li L 

PROVIDER: S-EPMC12453842 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature