%0 Journal Article
%T Transfer-Learning for Automated  Seizure Detection Based on Electric  Field Encephalography  Reconstructed Signal
%A Gefei Zhu
%J Communications and Network
%P 174-198
%@ 1947-3826
%D 2020
%I Scientific Research Publishing
%R 10.4236/cn.2020.124009
%X Building an automatic seizure onset prediction model based on
multi-channel electroencephalography (EEG) signals has been a hot topic in computer science and
neuroscience field for a long time. In this research, we collect EEG data from
different epilepsy patients and EEG devices and reconstruct and combine the EEG
signals using an innovative electric field encephalography (EFEG) method, which
establishes a virtual electric field vector, enabling extraction of electric
field components and increasing detection accuracy compared to the conventional
method. We extract a number of important features from the reconstructed
signals and pass them through an ensemble model based on support vector machine
(SVM), Random Forest (RF), and deep neural network (DNN) classifiers. By
applying this EFEG channel combination method, we can achieve the highest detection accuracy
at 87% which is 6% to 17% higher than the conventional channel averaging combination method. Meanwhile, to reduce
the potential overfitting problem caused by DNN models on a small dataset and
limited training patient, we ensemble the DNN model with two ˇ°weakerˇ±
classifiers to ensure the best performance in model transferring for different patients. Based on
these methods, we can achieve the highest detection accuracy at 82% on a new patient using a different EEG
device. Thus, we believe our method has good potential to be applied on
different commercial and clinical devices.
%K EEG
%K Seizure Detection
%K Transfer Learning
%K Deep Neural Network
%K Electric Field Encephalography
%U http://www.scirp.org/journal/PaperInformation.aspx?PaperID=104322