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
Sid Pramod, 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.