SeizureNet: Multi-Spectral Deep Feature Learning for Seizure Type Classification
This work addresses the challenge of precise diagnosis and efficient management of epilepsy for patients and clinicians, but it is incremental as it builds on existing deep learning methods for EEG analysis.
The paper tackles the problem of automatic classification of epileptic seizure types in EEG data, achieving a weighted F1 score of up to 0.94 for seizure-wise cross-validation and 0.59 for patient-wise cross-validation.
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 cross-patient seizure type classification. We used the recently released TUH EEG Seizure Corpus (V1.4.0 and V1.5.2) to evaluate the performance of SeizureNet. Experiments show that SeizureNet can reach a weighted F1 score of up to 0.94 for seizure-wise cross validation and 0.59 for patient-wise cross validation for scalp EEG based multi-class seizure type classification. We also show that the high-level feature embeddings learnt by SeizureNet considerably improve the accuracy of smaller networks through knowledge distillation for applications with low-memory constraints.