Classification of Normal and Hypoxia EEG Based on Hilbert Huang Transform

This paper reports a novel method to classify EEGs from subjects under normal and hypoxia conditions, which provides a potential efficient method to evaluate hypoxia in real time. The EEG data are collected from 3 healthy subjects while their neurobehaviors are evaluated to assess the degree of hypoxia. The specific energy in a sub-band of 30-60 Hz of the Hilbert Huang transform is extracted as the features. A linear discriminant classifier is utilized for classification. The experimental results show that the hypoxia EEG can be distinguished from normal one for individuals and the classification accuracy varies with the degree of hypoxia