  We apply convolutional neural networks (ConvNets) to the task of
distinguishing pathological from normal EEG recordings in the Temple University
Hospital EEG Abnormal Corpus. We use two basic, shallow and deep ConvNet
architectures recently shown to decode task-related information from EEG at
least as well as established algorithms designed for this purpose. In decoding
EEG pathology, both ConvNets reached substantially better accuracies (about 6%
better, ~85% vs. ~79%) than the only published result for this dataset, and
were still better when using only 1 minute of each recording for training and
only six seconds of each recording for testing. We used automated methods to
optimize architectural hyperparameters and found intriguingly different ConvNet
architectures, e.g., with max pooling as the only nonlinearity. Visualizations
of the ConvNet decoding behavior showed that they used spectral power changes
in the delta (0-4 Hz) and theta (4-8 Hz) frequency range, possibly alongside
other features, consistent with expectations derived from spectral analysis of
the EEG data and from the textual medical reports. Analysis of the textual
medical reports also highlighted the potential for accuracy increases by
integrating contextual information, such as the age of subjects. In summary,
the ConvNets and visualization techniques used in this study constitute a next
step towards clinically useful automated EEG diagnosis and establish a new
baseline for future work on this topic.
