Deep learning for epileptic intracranial EEG data
Abstract
Detection algorithms for electroencephalography (EEG) data typically employ handcrafted features that take advantage of the signal's specific properties. In the field of interictal epileptic discharge (IED) detection, the feature representation that provides optimal classification performance is still an unresolved issue. In this paper, we consider deep learning for automatic feature generation from epileptic intracranial EEG data in the time domain. Specifically, we consider convolutional neural networks (CNNs) in a subject independent fashion and demonstrate that meaningful features, representing IEDs are automatically learned. The resulting model achieves state of the art classification performance, provides insights for the different types of IEDs within the group, and is invariant to time differences between the IEDs. This study suggests that automatic feature generation via deep learning is suitable for IEDs and EEG in general.
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- Authors
- Antoniades, A., Spyrou, L., Took, C. C., & Sanei, S.
- Affiliations
- University of Surrey (GB)
- Venue
- 2016 IEEE 26th International Workshop on Machine Learning for Signal Processing (MLSP)
- Publisher
- IEEE
- Published
- 2016-09
- Pages
- 1-6
- ISSN
- 1551-2541 (ISSN-L)
- OpenAlex
- W2553015255
- Cited by
- 129
- References
- 13
- Citations by year
- 2026: 2 · 2025: 7 · 2024: 6 · 2023: 16 · 2022: 18 · 2021: 17 · 2020: 25 · 2019: 20 · 2018: 13 · 2017: 5
- FWCI
- 21.73
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- Machine learning for signal processing ... (publishedVersion)
- Surrey Research Insight Open Access (The University of Surrey) (submittedVersion)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › EEG and Brain-Computer Interfaces
- Health Sciences › Medicine › Psychiatry and Mental health › Epilepsy research and treatment
- Health Sciences › Medicine › Cardiology and Cardiovascular Medicine › ECG Monitoring and Analysis
- Keywords
- interictal epileptiform discharges, intracranial EEG, seizure detection, automatic feature learning, convolutional neural networks, interictal EEG
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