The DOI matches its Crossref record; 8 of the 8 references it cites were found in a registry or an index.
Focal Onset Seizure Prediction Using Convolutional Networks
Open accessAbstract
OBJECTIVE: This paper investigates the hypothesis that focal seizures can be predicted using scalp electroencephalogram (EEG) data. Our first aim is to learn features that distinguish between the interictal and preictal regions. The second aim is to define a prediction horizon in which the prediction is as accurate and as early as possible, clearly two competing objectives. METHODS: Convolutional filters on the wavelet transformation of the EEG signal are used to define and learn quantitative signatures for each period: interictal, preictal, and ictal. The optimal seizure prediction horizon is also learned from the data as opposed to making an a priori assumption. RESULTS: Computational solutions to the optimization problem indicate a 10-min seizure prediction horizon. This result is verified by measuring Kullback-Leibler divergence on the distributions of the automatically extracted features. CONCLUSION: The results on the EEG database of 204 recordings demonstrate that (i) the preictal phase transition occurs approximately ten minutes before seizure onset, and (ii) the prediction results on the test set are promising, with a sensitivity of 87.8% and a low false prediction rate of 0.142 FP/h. Our results significantly outperform a random predictor and other seizure prediction algorithms. SIGNIFICANCE: We demonstrate that a robust set of features can be learned from scalp EEG that characterize the preictal state of focal seizures.
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Epilepsy Seizure Prediction on EEG Using Common Spatial Pattern and Convolutional Neural Network
Zhang, Y., Guo, Y., Yang, P., Chen, W., & Lo, B. (2020) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2019.2933046 · Cited by 319
Li, Y., Liu, Y., Guo, Y.-Z., Liao, X.-F., Hu, B., & Yu, T. (2022) · IEEE Transactions on Cybernetics · doi:10.1109/tcyb.2021.3071860 · Cited by 196
B2-ViT net: broad vision transformer network with broad attention for seizure prediction
Shi, S., & Liu, W. (2024) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2023.3346955 · Cited by 124
Data leakage in deep learning studies of translational EEG
Brookshire, G., Kasper, J., Blauch, N. M., Wu, Y. C., Glatt, R., Merrill, D. A., Gerrol, S., Yoder, K. J., Quirk, C., & Lucero, C. (2024) · Frontiers in Neuroscience · doi:10.3389/fnins.2024.1373515 · Cited by 103
“Patient-specific seizure prediction using the convolutional neural networks,”
Jana, R., Bhattacharyya, S., & Das, S. (2020) · Advances in intelligent systems and computing · doi:10.1007/978-981-15-2021-1_7 · Cited by 9
Metadata
- Authors
- Khan, H., Marcuse, L., Fields, M., Swann, K., & Yener, B.
- Venue
- IEEE Transactions on Biomedical Engineering
- Published
- 2018-09
- Volume
- 65
- Issue
- 9
- Pages
- 2109-2118
- ISSN
- 0018-9294 (ISSN-L)
- 1558-2531
- OpenAlex
- W2780723646
- Cited by
- 353
- References
- 8
- FWCI
- 10.16
- Type
- journal article
- Language
- en
- Open access
- green
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