The primary goal of this project is to develop a system that can
automatically interpret an electroencephalography (EEG) test, thereby
improving the quality and efficiency of a physician’s diagnostic
capabilities. The demand for monitoring systems based on EEG signals
is growing rapidly, as they are increasingly being used for preventive
diagnostic procedures. The recent emergence of a comprehensive big
data resource, the TUH EEG database, has created a unique opportunity
to apply state of the art machine learning algorithms to this problem.
We propose the application of deep learning to classify EEGs and automatically generate time-aligned markers indicating areas of interest for physicians, enabling real-time alerting and automatic generation of a physician’s report. This will also enable mining of vast archives of EEG data to discover new ways to analyze and interpret EEGs. Finally, a large searchable database of marked up EEGs will be an invaluable resource for training medical students.