Note: This is a virtual presentation. Here is the link for where the presentation will be taking place.
Title: Localizing Seizure Foci with Deep Neural Networks and Graphical Models
Abstract: Worldwide estimates of the prevalence of epilepsy range from 1-3% of the total population, making it one of the most common neurological disorders. With its wide prevalence and dramatic effects on quality of life, epilepsy represents a large and ongoing public health challenge. Critical to the treatment of focal epilepsy is the localization of the seizure onset zone. The seizure onset zone is defined as the region of the cortex responsible for the generation of seizures. In the clinic, scalp electroencephalography (EEG) recording is the first modality used to localize the seizure onset zone.
My work focuses on developing machine learning techniques to localize this zone from these recordings. Using Bayesian techniques, I will present graphical models designed to captures the observed spreading of seizures in clinical EEG recordings. These models directly encode clinically observed seizure spreading phenomena to capture seizure onset and evolution. Using neural networks, the raw EEG signal is evaluated is evaluated for seizure activity. In this talk I will propose extensions to these techniques employing semi-supervised learning and architectural improvements for training sophisticated neural networks designed to analyze scalp EEG signals. In addition, I will propose modeling improvements to current graphical models for evaluating the confidence of localization results.
Archana Venkataraman (Department of Electrical and Computer Engineering)
Sri Sarma (Department of Biomedical Engineering)
Rene Vidal (Department of Biomedical Engineering)
Richard Leahy (Department of Electrical Engineering Systems – University of Southern California)