UMBC High Performance Computing Facility
Please note that this page is under construction. We are documenting the
240-node cluster maya that will be available after Summer 2014.
Currently, the 84-node cluster tara still operates independently,
until it becomes part of maya at the end of Summer 2014.
Please see the 2013 Resources Pages under the Resources tab for tara information.
Algorithm Characterization and Implementation for
Large Volume, High Resolution Multichannel
Electroencephalography Data in Seizure Detection
Tinoosh Mohsenin, Computer Science and Electrical Engineering
Adam Page, Computer Science and Electrical Engineering
Amey Kulkarni, Computer Science and Electrical Engineering
Tim Oates, Computer Science and Electrical Engineering
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 large volue of high resolution,
multi-channel 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. We also present the implementation
of these algorithms on different hardware approaches including
Virtex-7 FPGA, GPUs and 65 nm-CMOS ASIC.