The DOI matches its Crossref record; 35 of the 35 references it cites were found in a registry or an index.
Epilepsy Seizure Prediction on EEG Using Common Spatial Pattern and Convolutional Neural Network
Abstract
Epilepsy seizure prediction paves the way of timely warning for patients to take more active and effective intervention measures. Compared to seizure detection that only identifies the inter-ictal state and the ictal state, far fewer researches have been conducted on seizure prediction because the high similarity makes it challenging to distinguish between the pre-ictal state and the inter-ictal state. In this paper, a novel solution on seizure prediction is proposed using common spatial pattern (CSP) and convolutional neural network (CNN). Firstly, artificial pre-ictal EEG signals based on the original ones are generated by combining the segmented pre-ictal signals to solve the trial imbalance problem between the two states. Secondly, a feature extractor employing wavelet packet decomposition and CSP is designed to extract the distinguishing features in both the time domain and the frequency domain. It can improve overall accuracy while reducing the training time. Finally, a shallow CNN is applied to discriminate between the pre-ictal state and the inter-ictal state. Our proposed solution is evaluated on 23 patients' data from Boston Children's Hospital-MIT scalp EEG dataset by employing a leave-one-out cross-validation, and it achieves a sensitivity of 92.2% and false prediction rate of 0.12/h. Experimental result demonstrates that the proposed approach outperforms most state-of-the-art methods.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Artificial intelligence: a survey on evolution, models, applications and future trends
Lu, Y. (2019) · Journal of Management Analytics · doi:10.1080/23270012.2019.1570365 · Cited by 641
Truong, N. D., Nguyen, A. D., Kuhlmann, L., Bonyadi, M. R., Yang, J., Ippolito, S., & Kavehei, O. (2018) · Neural Networks · doi:10.1016/j.neunet.2018.04.018 · Cited by 635
Tsiouris, Κ. Μ., Pezoulas, V. C., Zervakis, M., Konitsiotis, S., Koutsouris, D. D., & Fotiadis, D. I. (2018) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2018.05.019 · Cited by 632
Adaptive epileptic seizure prediction system
Iasemidis, L. D., Shiau, D.-S., Chaovalitwongse, W., Sackellares, J. C., Pardalos, P. M., Principe, J. C., Carney, P. R., Prasad, A., Veeramani, B., & Tsakalis, K. (2003) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2003.810689 · Cited by 418
Seizure prediction with spectral power of EEG using cost-sensitive support vector machines
Park, Y., Luo, L., Parhi, K. K., & Netoff, T. (2011) · Epilepsia · doi:10.1111/j.1528-1167.2011.03138.x · Cited by 398
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
Focal Onset Seizure Prediction Using Convolutional Networks
Khan, H., Marcuse, L., Fields, M., Swann, K., & Yener, B. (2018) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2017.2785401 · Cited by 353
Lotte, F. (2015) · Proceedings of the IEEE · doi:10.1109/jproc.2015.2404941 · Cited by 327
Maiwald, T., Winterhalder, M., Aschenbrenner-Scheibe, R., Voss, H. U., Schulze-Bonhage, A., & Timmer, J. (2004) · Physica D: Nonlinear Phenomena · doi:10.1016/j.physd.2004.02.013 · Cited by 299
Epileptic seizure prediction using relative spectral power features
Bandarabadi, M., Teixeira, C. A., Rasekhi, J., & Dourado, A. (2015) · Clinical Neurophysiology · doi:10.1016/j.clinph.2014.05.022 · Cited by 262
Cited by
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
Self-citationB2-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
Metadata
- Authors
- Zhang, Y., Guo, Y., Yang, P., Chen, W., & Lo, B.
- Venue
- IEEE Journal of Biomedical and Health Informatics
- Published
- 2020-02
- Volume
- 24
- Issue
- 2
- Pages
- 465-474
- ISSN
- 2168-2194 (ISSN-L)
- 2168-2208
- OpenAlex
- W2965277555
- Cited by
- 319
- References
- 35
- FWCI
- 16.90
- Type
- journal article
- Language
- en
- Open access
- closed
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Epilepsy Seizure Prediction on EEG Using Common Spatial Pattern and Convolutional Neural Network. https://app.cochat.ai/papers/10.1109/jbhi.2019.2933046
- The paper
- Zhang, Y., Guo, Y., Yang, P., Chen, W., & Lo, B. (2020). Epilepsy Seizure Prediction on EEG Using Common Spatial Pattern and Convolutional Neural Network. IEEE Journal of Biomedical and Health Informatics. https://doi.org/10.1109/jbhi.2019.2933046
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
