  A key task in clinical EEG interpretation is to classify a recording or
session as normal or abnormal. In machine learning approaches to this task,
recordings are typically divided into shorter windows for practical reasons,
and these windows inherit the label of their parent recording. We hypothesised
that window labels derived in this manner can be misleading for example,
windows without evident abnormalities can be labelled `abnormal' disrupting the
learning process and degrading performance. We explored two separable
approaches to mitigate this problem: increasing the window length and
introducing a second-stage model to arbitrate between the window-specific
predictions within a recording. Evaluating these methods on the Temple
University Hospital Abnormal EEG Corpus, we significantly improved
state-of-the-art average accuracy from 89.8 percent to 93.3 percent. This
result defies previous estimates of the upper limit for performance on this
dataset and represents a major step towards clinical translation of machine
learning approaches to this problem.
