  Epilepsy is a highly prevalent brain condition with many serious
complications arising from it. The majority of patients which present to a
clinic and undergo electroencephalogram (EEG) monitoring would be unlikely to
experience seizures during the examination period, thus the presence of
interictal epileptiform discharges (IEDs) become effective markers for the
diagnosis of epilepsy. Furthermore, IED shapes and patterns are highly variable
across individuals, yet trained experts are still able to identify them through
EEG recordings - meaning that commonalities exist across IEDs that an algorithm
can be trained on to detect and generalise to the larger population. This
research proposes an IED detection system for the binary classification of
epilepsy using scalp EEG recordings. The proposed system features an ensemble
based deep learning method to boost the performance of a residual convolutional
neural network, and a bidirectional long short-term memory network. This is
implemented using raw EEG data, sourced from Temple University Hospital's EEG
Epilepsy Corpus, and is found to outperform the current state of the art model
for IED detection across the same dataset. The achieved accuracy and Area Under
Curve (AUC) of 94.92% and 97.45% demonstrates the effectiveness of an ensemble
method, and that IED detection can be achieved with high performance using raw
scalp EEG data, thus showing promise for the proposed approach in clinical
settings.
