M.S. Thesis Defense: Xinyuan Ma

Friday, September 6, 2019
1:00 p.m.
1146, A.V. Williams Building
Maria Hoo
301 405 3681
mch@umd.edu

ANNOUNCEMENT: M.S. Thesis Defense
 
 
 
NAME: Xinyuan Ma
 
 
Advisory Committee:
Professor Robert Newcomb, Chair/Advisor
Professor Raj Shekhar
Professor Armand Makowski
 
 
Date/Time: Friday, Friday September 6, 2019 at 1pm 
 
 
Place: Room 1146, A.V. Williams Building
 
 
Title: MULTI FEATURE ANALYSIS OF EEG SIGNAL ON SEIZURE PATTERNS AND DEEP NEURAL STRUCTURES FOR DETECTION OF EPLEPTIC SEIZURES
 

This work investigates the EEG signal processing and seizure detection based on deep learning architectures. The research includes two major parts. In the first part we use wavelet decomposition to process the signals as time frequency bands and extract signal features. The second part is the machine learning model and deep learning architecture we developed for seizure pattern analysis. The extracted feature maps are aligned as image inputs into our convolutional neural network (CNN) model. And we proposed our combined CNN-LSTM model to process the EEG signals as we have layers to function as feature extractors. In cross validation experiments, our CNN feature model could reach prediction accuracy of 96% and our CNN-LSTM model could reach 98% and 94% on average. In the developable work we proposed a matching network architecture which employs two parallel channels of our models to improve the sensitivity.

 

Audience: Graduate  Faculty 

 

September 2019

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