Adversarial Representation Learning for Robust Patient-Independent Epileptic Seizure Detection

Epilepsy is a chronic neurological disorder characterized by the occurrence of spontaneous seizures, which affects about one percent of the worlds 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. 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. 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. 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.

Authors

• Xiang Zhang Northwest University of China
• Lina Yao CSIRO’s Data61
• Manqing Dong
• Zhe Liu Zhejiang Lab
• Yu Zhang
• Yong Li

Keywords

• Informatics
• Generative Adversarial Networks
• Representation Learning
• Epileptic Seizures
• Robust Detection
• Seizure Detection
• Epileptic Seizure Detection
• Adversarial Representation Learning
• Deep Neural Network
• Attention Mechanism
• Raw Signal
• EEG Signals
• Low Latency
• Important Channel
• Feature Engineering
• Adversarial Training
• EEG Channels
• Automatic Learning
• Raw EEG Signals
• Loss Function
• Brain Regions
• Latent Representation
• Convolutional Layers
• EEG Samples
• Temporal Lobe
• FC Layer
• Convolutional Neural Network
• Latent Features
• Max-pooling Layer
• Latent Space
• Hyperparameter Settings
• Non-invasive EEG
• patient-independent
• adversarial deep learning
• Brain
• Deep Learning
• Diagnosis, Computer-Assisted
• Electroencephalography
• Humans
• Neural Networks, Computer
• Seizures
• Signal Processing, Computer-Assisted