Unsupervised learning from EEG data for epilepsy: A systematic literature review

Abstract

Background and objectives: Epilepsy is a neurological disorder characterized by recurrent epileptic seizures, whose neurophysiological signature is altered electroencephalographic (EEG) activity. The use of artificial intelligence (AI) methods on EEG data can positively impact the management of the disease, significantly improving diagnostic and prognostic accuracy as well as treatment outcomes. Our work aims to systematically review the available literature on the use of unsupervised machine learning methods on EEG data in epilepsy, focusing on methodological and clinical differences in terms of algorithms used and clinical applications.

Methods: Following the PRISMA guidelines, a systematic literature search was performed in several databases for papers published in the last 10 years. Studies employing both unsupervised and self-supervised methods for the classification of EEG data in epilepsy patients were included. The main outcomes of the study were: (i) to provide an overview of the datasets used as input to train the algorithms; (ii) to identify trends in pre-processing, algorithm architectures, validation, and metrics for performance estimation; (iii) to identify and review the clinical applications of AI in epilepsy patients.

Results: A total of 108 studies met the inclusion criteria. Of them, 86 (79.6 %) have been published in the last 5 years and 60 (55.5 %) in the last two years. The most used validation methods were: hold-out in 37 (34.2 %), k-fold-cross validation in 35 (32.4 %), and leave-one-out in 19 (17.6 %) studies, respectively. Accuracy, sensitivity, and specificity were the most used performance metrics being reported in 71 (65.7 %), 62 (57.4 %), and 42 (39.8 %) studies, respectively, followed by F1-score (27 studies; 25 %), precision (26 studies; 24 %), area under the curve (25 studies; 23.1 %), and false positive rate (22 studies; 20.3 %). Furthermore, 42 (38.9 %) compared to 63 (58.3 %) studies used individual patient versus multiple patients models, respectively. Finally, concerning the clinical applications of unsupervised learning methods on epilepsy patients, we identified six main fields of interest: seizure detection (69 studies; 63.9 %), seizure prediction (27 studies; 25 %), signal propagation and characterization (2 studies; 1.8 %), seizure localization (4 studies; 3.7 %), and seizure classification (22 studies; 20.3 %), respectively.

Conclusion: The results of this review suggest that the interest in the use of unsupervised learning methods in epilepsy has significantly increased in recent years. From a methodological perspective, the input EEG datasets used for training and testing the algorithms remain the hardest challenge. From a clinical standpoint, the vast majority of studies addressed seizure detection, prediction, and classification whereas studies focusing on seizure characterization and localization are lacking. Future work that can potentially improve the performance of these algorithms includes the use of context information via reinforcement learning and a focus on model explainability.