  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 paper 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.
