A Machine Learning Approach for Sex and Age Classification of Paediatric EEGs

Abstract

Electroencephalography (EEG) is an important investigation of childhood seizures and other brain disorders. Expert visual analysis of EEGs can estimate subjects' age based on the presence of particular maturational features. The sex of a child, however, cannot be determined by visual inspection. In this study, we explored sex and age differences in the EEGs of 351 healthy male and female children aged between 6 and 10 years. We developed machine learning-based methods to classify the sex and age of healthy children from their EEGs. This preliminary study based on small EEG numbers demonstrates the potential for machine learning in helping with age determination in healthy children. This may be useful in distinguishing developmentally normal from developmentally delayed children. The model performed poorly for estimation of biological sex. However, we achieved 66.67% accuracy in age prediction allowing a 1 year error, on the test set.