Geometric Deep Learning for Subject Independent Epileptic Seizure Prediction Using Scalp EEG Signals

• Published in: IEEE journal of biomedical and health informatics Vol. 26; no. 2; pp. 527 - 538
• Main Authors: Dissanayake, Theekshana, Fernando, Tharindu, Denman, Simon, Sridharan, Sridha, Fookes, Clinton
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.02.2022; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Anomalies; Brain modeling; Convulsions & seizures; Data models; Deep Learning; EEG; Electroencephalography; Electroencephalography - methods; Epilepsy; Graphs; Humans; Localization; Machine learning; neural networks; Prediction models; Predictive models; Scalp; seizure prediction; Seizures; Seizures - diagnosis; signal processing; Synthesis; Training
• ISSN: 2168-2194; 2168-2208; 2168-2208
• DOI: 10.1109/JBHI.2021.3100297

Recently, researchers in the biomedical community have introduced deep learning-based epileptic seizure prediction models using electroencephalograms (EEGs) that can anticipate an epileptic seizure by differentiating between the pre-ictal and interictal stages of the subject's brain. Despite having the appearance of a typical anomaly detection task, this problem is complicated by subject-specific characteristics in EEG data. Therefore, studies that investigate seizure prediction widely employ subject-specific models. However, this approach is not suitable in situations where a target subject has limited (or no) data for training. Subject-independent models can address this issue by learning to predict seizures from multiple subjects, and therefore are of greater value in practice. In this study, we propose a subject-independent seizure predictor using Geometric Deep Learning (GDL). In the first stage of our GDL-based method we use graphs derived from physical connections in the EEG grid. We subsequently seek to synthesize subject-specific graphs using deep learning. The models proposed in both stages achieve state-of-the-art performance using a one-hour early seizure prediction window on two benchmark datasets (CHB-MIT-EEG: 95.38% with 23 subjects and Siena-EEG: 96.05% with 15 subjects). To the best of our knowledge, this is the first study that proposes synthesizing subject-specific graphs for seizure prediction. Furthermore, through model interpretation we outline how this method can potentially contribute towards Scalp EEG-based seizure localization.