Title: Gated Recurrent Networks for Seizure Detection,
Abstract: Recurrent Neural Networks (RNNs) with sophisticated units that implement a
gating mechanism have emerged as powerful technique for modeling sequential
signals such as speech or electroencephalography (EEG). The latter is the focus
on this paper. A significant big data resource, known as the TUH EEG Corpus
(TUEEG), has recently become available for EEG research, creating a unique
opportunity to evaluate these recurrent units on the task of seizure detection.
In this study, we compare two types of recurrent units: long short-term memory
units (LSTM) and gated recurrent units (GRU). These are evaluated using a state
of the art hybrid architecture that integrates Convolutional Neural Networks
(CNNs) with RNNs. We also investigate a variety of initialization methods and
show that initialization is crucial since poorly initialized networks cannot be
trained. Furthermore, we explore regularization of these convolutional gated
recurrent networks to address the problem of overfitting. Our experiments
revealed that convolutional LSTM networks can achieve significantly better
performance than convolutional GRU networks. The convolutional LSTM
architecture with proper initialization and regularization delivers 30%
sensitivity at 6 false alarms per 24 hours.