Spike sleep state deep learning classifier

Epilepsy is a difficult disorder to assess and even more so automatically. It is observed that in REM sleep epileptiform activity differs from other states of consciousness. REM sleep could hold the key to a better understanding of epilepsy, however robust features that work on different people and types of epilepsies are required. The purpose of using a deep learning model to classify epileptic spikes into designated sleep states can help designate those robust features. Instead of blindly looking for answers the deep learning model can incorporate millions of features where possibly only a handful are representative of why a spike is classified into REM. Through this work the features discovered could lead to a better understanding of epilepsy and hopefully better treatment.

Faculty Supervisor:

Marcus Ng;Zahra Kazem-Moussavi

Student:

Partner:

Epilepsy and Seizure Association of Manitoba

Discipline:

Engineering

Sector:

Professional, scientific and technical services

University:

University of Manitoba

Program: