A channel-wise attention-based representation learning method for epileptic seizure detection and type classification

Epilepsy affect almost 1% of the worldwide population. An early diagnosis of seizure types is a

crucial patient-dependent step for the treatment selection process. The selection of the proper treatment

relies on the correct identification of the seizure type. As such, identifying the seizure type has the

biggest immediate influence on therapy than the seizure detection, reducing the neurologist’s efforts

when reading and detecting seizures in EEG recordings. Most of the existing seizure detection and

classification methods are conceptualized following the patient-dependent schema thus fail to perform

well with unknown cases. Our work focuses on patient-independent schema for seizure type classification

and pays more attention to the explainability of the underlying attention mechanism of our method.

Using a channel-wise attention mechanism, a quantification of the EEG channels contribution is enabled.

Therefore, results become more interpretable and a visualization of brain lobes contribution by seizure

types is allowed. We evaluate our model for seizure detection and type classification on CHB-MIT and

the recently released TUH EEG Seizure, respectively. Our model is able to classify 8 seizure types with

an accuracy of 98.41%, directly from raw EEG data without any preprocessing. A case study showed a

high correlation between the neurological baselines and the interpretable results of our model.