  Brain-related disorders such as epilepsy can be diagnosed by analyzing
electroencephalograms (EEG). However, manual analysis of EEG data requires
highly trained clinicians, and is a procedure that is known to have relatively
low inter-rater agreement (IRA). Moreover, the volume of the data and the rate
at which new data becomes available make manual interpretation a
time-consuming, resource-hungry, and expensive process. In contrast, automated
analysis of EEG data offers the potential to improve the quality of patient
care by shortening the time to diagnosis and reducing manual error. In this
paper, we focus on one of the first steps in interpreting an EEG session -
identifying whether the brain activity is abnormal or normal. To solve this
task, we propose a novel recurrent neural network (RNN) architecture termed
ChronoNet which is inspired by recent developments from the field of image
classification and designed to work efficiently with EEG data. ChronoNet is
formed by stacking multiple 1D convolution layers followed by deep gated
recurrent unit (GRU) layers where each 1D convolution layer uses multiple
filters of exponentially varying lengths and the stacked GRU layers are densely
connected in a feed-forward manner. We used the recently released TUH Abnormal
EEG Corpus dataset for evaluating the performance of ChronoNet. Unlike previous
studies using this dataset, ChronoNet directly takes time-series EEG as input
and learns meaningful representations of brain activity patterns. ChronoNet
outperforms the previously reported best results by 7.79% thereby setting a new
benchmark for this dataset. Furthermore, we demonstrate the domain-independent
nature of ChronoNet by successfully applying it to classify speech commands.
