Unsupervised Multivariate Time-Series Transformers for Seizure Identification on EEG

TL;DR

This paper introduces the first unsupervised transformer-based autoencoder model for seizure detection in EEG data, leveraging a novel masking strategy and outperforming supervised methods on benchmark datasets.

Contribution

The work presents a novel unsupervised transformer autoencoder for EEG seizure detection, eliminating the need for labeled data and improving performance on imbalanced datasets.

Findings

Outperforms supervised methods with up to 16% higher recall

Achieves up to 9% better accuracy and AUC

Effective on multiple benchmark EEG datasets

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.