Unsupervised Multivariate Time-Series Transformers for Seizure Identification on EEG

ILKAY YILDIZ POTTER, George Zerveas, Carsten Eickhoff, Dominique Duncan

Abstract:

Epilepsy is one of the most common neurological disorders, typically observed via seizure episodes. Epileptic
seizures are commonly monitored through electroencephalogram
(EEG) recordings due to their routine and low expense collection.
The stochastic nature of EEG makes seizure identification via
manual inspections performed by highly-trained experts a tedious
endeavor, motivating the use of automated identification. The literature on automated identification focuses mostly on supervised
learning methods requiring expert labels of EEG segments that
contain seizures, which are difficult to obtain. Motivated by these
observations, we pose seizure identification as an unsupervised
anomaly detection problem. To this end, we employ the first
unsupervised transformer-based model for seizure identification
on raw EEG. We train an autoencoder involving a transformer
encoder via an unsupervised loss function, incorporating a novel
masking strategy uniquely designed for multivariate time-series
data such as EEG. Training employs EEG recordings that do not
contain any seizures, while seizures are identified with respect
to reconstruction errors at inference time. We evaluate our
method on three publicly available benchmark EEG datasets
for distinguishing seizure vs. non-seizure windows. Our method
leads to significantly better seizure identification performance
than supervised learning counterparts, by up to 16% recall, 9%
accuracy, and 9% Area under the Receiver Operating Characteristics Curve (AUC), establishing particular benefits on highly
imbalanced data. Through accurate seizure identification, our
method could facilitate widely accessible and early detection of
epilepsy development, without needing expensive label collection
or manual feature extraction.