Adversarial Representation Learning for Robust Patient-Independent Epileptic Seizure Detection

TL;DR

This paper introduces a robust, explainable deep learning model for patient-independent epileptic seizure detection using raw EEG signals, outperforming existing methods and providing insights for clinical analysis.

Contribution

It presents a novel adversarial training framework with an attention mechanism to learn seizure-specific EEG representations without manual feature extraction.

Findings

Outperforms state-of-the-art baselines on TUH EEG database

Provides fine-grained interpretability for seizure diagnosis

Demonstrates low latency suitable for real-time detection

Abstract

Objective: Epilepsy is a chronic neurological disorder characterized by the occurrence of spontaneous seizures, which affects about one percent of the world's population. Most of the current seizure detection approaches strongly rely on patient history records and thus fail in the patient-independent situation of detecting the new patients. To overcome such limitation, we propose a robust and explainable epileptic seizure detection model that effectively learns from seizure states while eliminates the inter-patient noises. Methods: A complex deep neural network model is proposed to learn the pure seizure-specific representation from the raw non-invasive electroencephalography (EEG) signals through adversarial training. Furthermore, to enhance the explainability, we develop an attention mechanism to automatically learn the importance of each EEG channels in the seizure diagnosis procedure. Results: The proposed approach is evaluated over the Temple University Hospital EEG (TUH EEG) database. The experimental results illustrate that our model outperforms the competitive state-of-the-art baselines with low latency. Moreover, the designed attention mechanism is demonstrated ables to provide fine-grained information for pathological analysis. Conclusion and significance: We propose an effective and efficient patient-independent diagnosis approach of epileptic seizure based on raw EEG signals without manually feature engineering, which is a step toward the development of large-scale deployment for real-life use.