SeizureNet: Multi-Spectral Deep Feature Learning for Seizure Type Classification

Automatic classification of epileptic seizure types in electroencephalograms (EEGs) data can enable more precise diagnosis and efficient management of the disease. This task is challenging due to factors such as low signal-to-noise ratios, signal artefacts, high variance in seizure semiology among epileptic patients, and limited availability of clinical data. To overcome these challenges, in this paper, we present SeizureNet, a deep learning framework which learns multi-spectral feature embeddings using an ensemble architecture for accurate cross-patient seizure type classification. Experiments on the recently released TUH EEG Seizure Corpus show that SeizureNet produces state-of-the-art weighted F1 scores of 0.98 for seizure type classification setting new benchmarks on the dataset. We also show that the high-level feature embeddings learnt by SeizureNet considerably improve the classification accuracy of smaller networks through knowledge distillation for applications with low-memory and fast inference speed requirements.