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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ImageNet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017) · Communications of the ACM · doi:10.1145/3065386 · Cited by 112,155
SMOTE synthetic minority over-sampling technique
Chawla, N. V., Bowyer, K. W., Hall, L. O., & Kegelmeyer, W. P. (2002) · Journal of Artificial Intelligence Research · doi:10.1613/jair.953 · Cited by 32,865
Matthews, D. R., Hosker, J. P., Rudenski, A. S., Naylor, B. A., Treacher, D. F., & Turner, R. C. (1985) · Diabetologia · doi:10.1007/bf00280883 · Cited by 31,502
Goldberger, A. L., Amaral, L. A. N., Glass, L., Hausdorff, J. M., Ivanov, P. C., Mark, R. G., Mietus, J. E., Moody, G. B., Peng, C.-K., & Stanley, H. E. (2000) · Circulation · doi:10.1161/01.cir.101.23.e215 · Cited by 14,809
The wavelet transform, time-frequency localization and signal analysis
Daubechies, I. (1990) · IEEE Transactions on Information Theory · doi:10.1109/18.57199 · Cited by 6,483
ADADELTA: An Adaptive Learning Rate Method
Zeiler, M. D. (2012) · arXiv · doi:10.48550/arxiv.1212.5701 · Cited by 5,322
Classification of patterns of EEG synchronization for seizure prediction
Mirowski, P., Madhavan, D., LeCun, Y., & Kuzniecky, R. (2009) · Clinical Neurophysiology · doi:10.1016/j.clinph.2009.09.002 · Cited by 380
A forward-looking review of seizure prediction
Freestone, D. R., Karoly, P. J., & Cook, M. J. (2017) · Current Opinion in Neurology · doi:10.1097/wco.0000000000000429 · Cited by 169
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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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- Khan, H., Marcuse, L., Fields, M., Swann, K., & Yener, B. (2018). Focal Onset Seizure Prediction Using Convolutional Networks. IEEE Transactions on Biomedical Engineering. https://doi.org/10.1109/tbme.2017.2785401
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