Qeeg-Based Classification With Wavelet Packet and Microstate Features for Triage Applications in the ER

We describe methods for the classification of brain state using quantitative analysis of the EEG (QEEG). Neurometric analysis of EEG collected from the 19 standard locations of the International 10-20 System already provides such a tool. In this work we demonstrate the effectiveness of this approach when the available inputs are reduced to a set of five frontal electrodes. This system has applications in certain critical clinical care situations, such as emergency room triage, when a full EEG might be unavailable, inconvenient, or time-consuming. Additionally, we augment the standard neurometric QEEG analysis with local discriminant basis features of the power spectrum and microstate-like features which exploit the rich temporal structure of the EEG. These enhancements provide clear gains in sensitivity and specificity on a representative database