Title	Time series analysis using deep feedforward neural networks
Author	Turner, Jeffrey Thomas
Advisors	Oates, Tim ;
Program	Computer Science
UMBC Department	Computer Science and Electrical Engineering
Document Type	thesis
Sponsors	University of Maryland, Baltimore County (UMBC)
Keywords	Deep Learning; EEG; Machine Learning; Neural Network; Seizure; Time Series
Date Issued	2014-01-01
Abstract	Deep neural networks can be used for abstraction and as a preprocessing step for other machine learning classifiers. Our goal was to develop methods for a more accurate automated seizure detection. Deep architectures have been used for classification of events, and shown in this research to be an effective way of classifying multichannel high resolution medical data. The medical data used in this thesis was gathered from an electroencephalograph (EEG) used in a hospital setting on seizure patients. To demonstrate the ability of deep architectures to learn and abstract from input data, the signals from the EEG that contained both seizure and non seizure data were given both as featurized data and raw data to the deep architecture. In addition to the multiple types of data preparation, a patients EEG data was tested not only against their own EEG signal training data but other patients as well. This study supports the effectiveness of deep feed forward neural networks for usage in the seizure classification scenario, as well as highlights some of the difficulties associated with training deep neural networks, as shown through experimental results.
Identifier	11005
Format	application:pdf
Language	en
Collection	UMBC Thesis and Dissertations
Rights Statement	This item may be protected under Title 17 of the U.S. Copyright Law. It is made available by UMBC for non-commercial research and education. For permission to publish or reproduce, please see http://library.umbc.edu/speccoll/rightsreproductions.php or contact Special Collections at speccoll(at)umbc.edu
Source	Turner_umbc_0434M_11005.pdf
Access Rights	Access limited to the UMBC community. Item may possibly be obtained vis Interlibrary Loan through a local library, pending author/copyright holder's permission.

Time series analysis using deep feedforward neural networks

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