Abstract: While continuous EEG monitoring is increasingly used to improve outcomes of critical care patients, reviewing the EEG record in a timely manner is challenging. Before automated algorithms can be developed and compared with human clinicians, accurately annotated data is required. This project implements software system to enable a crowdsourcing solution to annotating EEG while providing quantitative estimates of the labelers’ accuracy and the overall accuracy of consensus annotations.

Learning Objective 1: Reader will be able to describe an approach to annotate EEG records using a crowdsourcing approach where annotators are EEG professionals.


Andrew Nguyen (Presenter)
University of San Francisco

William Bosl (Presenter)
University of San Francisco

Susan Herman, Beth Israel Deaconess Medical Center
Tobias Loddenkemper, Boston Children's Hospital

Presentation Materials: