Authors
Alireza Amirshahi, Anthony Thomas, Amir Aminifar, Tajana Rosing, David Atienza
Publication date
2022/9/22
Journal
IEEE Journal of Biomedical and Health Informatics
Volume
27
Issue
1
Pages
202-214
Publisher
IEEE
Description
Recent years have seen growing interest in leveraging deep learning models for monitoring epilepsy patients based on electroencephalographic (EEG) signals. However, these approaches often exhibit poor generalization when applied outside of the setting in which training data was collected. Furthermore, manual labeling of EEG signals is a time-consuming process requiring expert analysis, making fine-tuning patient-specific models to new settings a costly proposition. In this work, we propose the Maximum-Mean-Discrepancy Decoder (M2D2) for automatic temporal localization and labeling of seizures in long EEG recordings to assist medical experts. We show that M2D2 achieves 76.0% and 70.4% of F1-score for temporal localization when evaluated on EEG data gathered in a different clinical setting than the training data. The results demonstrate that M2D2 yields substantially higher generalization …
Total citations
2023202426
Scholar articles
A Amirshahi, A Thomas, A Aminifar, T Rosing… - IEEE Journal of Biomedical and Health Informatics, 2022