DeepAI AI Chat
Log In Sign Up

Gated Recurrent Networks for Seizure Detection

by   Meysam Golmohammadi, et al.

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.


page 1

page 2

page 3

page 4


Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling

In this paper we compare different types of recurrent units in recurrent...

Memory Visualization for Gated Recurrent Neural Networks in Speech Recognition

Recurrent neural networks (RNNs) have shown clear superiority in sequenc...

Affective EEG-Based Person Identification Using the Deep Learning Approach

There are several reports available on affective electroencephalography-...

Investigating gated recurrent neural networks for speech synthesis

Recently, recurrent neural networks (RNNs) as powerful sequence models h...

The effect of phased recurrent units in the classification of multiple catalogs of astronomical lightcurves

In the new era of very large telescopes, where data is crucial to expand...

Deep Architectures for Automated Seizure Detection in Scalp EEGs

Automated seizure detection using clinical electroencephalograms is a ch...

Recurrently Controlled Recurrent Networks

Recurrent neural networks (RNNs) such as long short-term memory and gate...