M2D2 : Maximum-Mean-Discrepancy Decoder for Temporal Localization of Epileptic Brain Activities
(2023) In IEEE Journal of Biomedical and Health Informatics 27(1). p.202-214- Abstract
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... (More)
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 performance than other state-of-the-art deep learning-based approaches.
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- author
- Amirshahi, Alireza ; Thomas, Anthony ; Aminifar, Amir LU ; Rosing, Tajana and Atienza, David
- organization
- publishing date
- 2023
- type
- Contribution to journal
- publication status
- published
- subject
- keywords
- Brain modeling, Decoding, Deep learning, Electroencephalography, Epileptic Seizure, Kernel, Location awareness, Maximum Mean Discrepancy, Non-invasive EEG, Recording, Temporal Localization
- in
- IEEE Journal of Biomedical and Health Informatics
- volume
- 27
- issue
- 1
- pages
- 13 pages
- publisher
- IEEE - Institute of Electrical and Electronics Engineers Inc.
- external identifiers
-
- scopus:85139421136
- pmid:36136930
- ISSN
- 2168-2194
- DOI
- 10.1109/JBHI.2022.3208780
- language
- English
- LU publication?
- yes
- id
- f552254c-f979-4b1e-a45e-1c153e75ba5d
- date added to LUP
- 2022-12-14 12:54:47
- date last changed
- 2024-04-14 10:02:33
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