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Seizure detection from multiple physiological signals

Tinoosh Mohsenin, Tim Oates, Adam Page, and Sid Pramod, Department of Computer Science and Electrical Engineering, UMBC

Ubiquitous bio-sensing for personalized health monitoring is slowly becoming a reality with the increasing availability of
small, diverse, robust, high fidelity sensors. This oncoming flood of data begs the question of how we will extract useful
information from it. In this project we explore the use of a variety of representations and machine learning algorithms applied to the task of seizure detection in high resolution, multichannel EEG data. We explore classification accuracy, computational complexity and memory requirements with a view toward understanding which approaches are most suitable for such tasks as the number of people involved and the amount of data they produce grows to be quite large. In particular, we show that layered learning approaches such as Deep Belief Networks excel along these dimensions.