AAAI Publications, The Twenty-Seventh International Flairs Conference

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Comparing Raw Data and Feature Extraction for Seizure Detection with Deep Learning Methods
Adam Page, J. T. Turner, Tinoosh Mohsenin, Tim Oates

Last modified: 2014-05-03


Personalized health monitoring is slowly becoming a reality due to advances in small, high-fidelity sensors, low-power processors, as well as energy harvesting techniques. The ability to efficiently and effectively process this data and extract useful information is of the utmost importance. In this paper, we aim at dealing with this challenge for the application of automated seizure detection. We explore the use of a variety of representations and machine learning algorithms to the particular task of seizure detection in high-resolution, multi-channel EEG data. In doing so, we explore the classification accuracy, computational complexity and memory requirements with a view toward understanding which approaches are most suitable. In particular, we show that layered learning approaches such as Deep Belief Networks excel along these dimensions.


Machine Learning; Smart Health; DBN; Seizure Detection; Artificial Intelligence

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