RT Journal Article
SR Electronic
T1 A Comparison of Deep Neural Networks for Seizure Detection in EEG Signals
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 702654
DO 10.1101/702654
A1 Poomipat Boonyakitanont
A1 Apiwat Lek-uthai
A1 Krisnachai Chomtho
A1 Jitkomut Songsiri
YR 2019
UL http://biorxiv.org/content/early/2019/07/15/702654.abstract
AB This paper aims to apply machine learning techniques to an automated epileptic seizure detection using EEG signals to help neurologists in a time-consuming diagnostic process. We employ two approaches based on convolution neural networks (CNNs) and artificial neural networks (ANNs) to provide a probability of seizure occurrence in a windowed EEG recording of 18 channels. In order to extract relevant features based on time, frequency, and time-frequency domains for these networks, we consider an improvement of the Bayesian error rate from a baseline. Features of which the improvement rates are higher than the significant level are considered. These dominant features extracted from all EEG channels are concatenated as the input for ANN with 7 hidden layers, while the input of CNN is taken as raw multi-channel EEG signals. Using multi-concept of deep CNN in image processing, we exploit 2D-filter decomposition to handle the signal in spatial and temporal domains. Our experiments based on CHB-MIT Scalp EEG Database showed that both ANN and CNN were able to perform with the overall accuracy of up to 99.07% and F1-score of up to 77.04%. ANN with dominant features is more capable of detecting seizure events than CNN whereas CNN requiring no feature extraction is slightly better than ANN in classification accuracy.