Algorithm Characterization and Implementation for Large Volume, High Resolution Multichannel Electroencephalography Data in Seizure Detection

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 value 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 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 FPGAs and 65 nm-CMOS ASIC.