  Epilepsy affects nearly 1% of the global population, of which two thirds can
be treated by anti-epileptic drugs and a much lower percentage by surgery.
Diagnostic procedures for epilepsy and monitoring are highly specialized and
labour-intensive. The accuracy of the diagnosis is also complicated by
overlapping medical symptoms, varying levels of experience and inter-observer
variability among clinical professions. This paper proposes a novel hybrid
bilinear deep learning network with an application in the clinical procedures
of epilepsy classification diagnosis, where the use of surface
electroencephalogram (sEEG) and audiovisual monitoring is standard practice.
Hybrid bilinear models based on two types of feature extractors, namely
Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs), are
trained using Short-Time Fourier Transform (STFT) of one-second sEEG. In the
proposed hybrid models, CNNs extract spatio-temporal patterns, while RNNs focus
on the characteristics of temporal dynamics in relatively longer intervals
given the same input data. Second-order features, based on interactions between
these spatio-temporal features are further explored by bilinear pooling and
used for epilepsy classification. Our proposed methods obtain an F1-score of
97.4% on the Temple University Hospital Seizure Corpus and 97.2% on the
EPILEPSIAE dataset, comparing favourably to existing benchmarks for sEEG-based
seizure type classification. The open-source implementation of this study is
available at https://github.com/NeuroSyd/Epileptic-Seizure-Classification
