Show simple item record

dc.contributor.advisor Jayarathna, Sampath en
dc.contributor.author Shimpi, Shubhangi en
dc.date.accessioned 2018-02-12T23:19:37Z en
dc.date.available 2018-02-12T23:19:37Z en
dc.date.issued 2018-02-12 en
dc.identifier.uri http://hdl.handle.net/10211.3/199949 en
dc.description.abstract Electroencephalogram (EEG) data includes information of electrical activity of a brain; thus is commonly used to diagnose any underlying neurological condition such as epilepsy. Epileptic patients are at risk of facing life threatening incidents when driving a vehicle or handling machinery. Hence it is important to detect the phases when the patients are more likely to have seizures. Manual detection of seizures is expensive because it involves visual examination of hours long of EEG data. There is a need of built-in seizure detection and prediction system to automatically classify the EEG signals into various types of signals using machine-learning techniques. In this project, we present a seizure detection model and seizure prediction model using recurrent neural networks (RNN). We propose various deep RNN models to predict seizures. To perform preliminary evaluation the accuracy for each of the model is evaluated using a publicly available dataset. LSTM model correctly classifies the EEG data with prediction accuracy of 96% whereas the lightweight GRU model demonstrates promising results with overall prediction accuracy of 98%. en
dc.format.extent 31 pgs. en
dc.language.iso en en
dc.publisher California State Polytechnic University, Pomona en
dc.rights.uri http://www.cpp.edu/~broncoscholar/rightsreserved.html en
dc.subject deep learning en
dc.subject recurrent neural networks en
dc.subject seizure prediction en
dc.subject epilepsy en
dc.title Deep Recurrent Neural Networks for Seizure Prediction in Epileptic Patients en
dc.type Graduate Project en
dc.contributor.department Department of Computer Science en
dc.description.degree M.S. en
dc.contributor.committeeMember Sun, Yu en
dc.contributor.committeeMember Ji, Dr. Hao en
dc.rights.license All rights reserved en

Files in this item


This item appears in the following Collection(s)

Show simple item record

Search DSpace

My Account

RSS Feeds