Title: Real-Time Seizure Detection using EEG - A Comprehensive Comparison of Recent Approaches under a Realistic Setting

Speaker: Hyewon Jeong

Abstract:
Electroencephalogram (EEG) is an important diagnostic test that physicians use to record brain activity and detect seizures by monitoring the signals. There have been several attempts to detect seizures and abnormalities in EEG signals with modern deep learning models to reduce the clinical burden. However, they cannot be fairly compared against each other as they were tested in distinct experimental settings. Also, some of them are not trained in real-time seizure detection tasks, making it hard for on-device applications. In this work, for the first time, we extensively compare multiple state-of-the-art models and signal feature extractors in a real-time seizure detection framework suitable for real-world application, using various evaluation metrics including a new one we propose to evaluate more practical aspects of seizure detection models.


Speaker Bio:
Hyewon Jeong, M.D., M.S. is a Ph.D. student in Electrical Engineering and Computer Science at MIT, co-advised by Marzyeh Ghassemi and Collin Stultz. Her primary research focus has been on developing and applying machine learning methods to solve real-world clinical tasks using time-series electronic health record data and signal data. Before joining MIT, she received a B.S. in Biological Sciences, M.S. in Computer Science from KAIST, and M.D. at Yonsei University.

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