The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Real-Time Epileptic Seizure Prediction Using AR Models and Support Vector Machines
Abstract
This paper addresses the prediction of epileptic seizures from the online analysis of EEG data. This problem is of paramount importance for the realization of monitoring/control units to be implanted on drug-resistant epileptic patients. The proposed solution relies in a novel way on autoregressive modeling of the EEG time series and combines a least-squares parameter estimator for EEG feature extraction along with a support vector machine (SVM) for binary classification between preictal/ictal and interictal states. This choice is characterized by low computational requirements compatible with a real-time implementation of the overall system. Moreover, experimental results on the Freiburg dataset exhibited correct prediction of all seizures (100 % sensitivity) and, due to a novel regularization of the SVM classifier based on the Kalman filter, also a low false alarm rate.
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Citations
Cites
Estimation with Applications to Tracking and Navigation: Theory, Algorithms and Software
Bar‐Shalom, Y., Li, X. ‐., & Kirubarajan, T. (2002) · doi:10.1002/0471221279 · Cited by 6,782
Cited by
Seizure detection, seizure prediction, and closed-loop warning systems in epilepsy
Ramgopal, S., Thome-Souza, S., Jackson, M., Kadish, N. E., Sánchez Fernández, I., Klehm, J., Bosl, W., Reinsberger, C., Schachter, S., & Loddenkemper, T. (2014) · Epilepsy & Behavior · doi:10.1016/j.yebeh.2014.06.023 · Cited by 469
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
A 48.6-to-105.2 µW Machine Learning Assisted Cardiac Sensor SoC for Mobile Healthcare Applications
Hsu, S.-Y., Ho, Y., Chang, P.-Y., Su, C., & Lee, C.-Y. (2014) · IEEE Journal of Solid-State Circuits · doi:10.1109/jssc.2013.2297406 · Cited by 82
Improved detection of Parkinsonian resting tremor with feature engineering and Kalman filtering
Yao, L., Brown, P., & Shoaran, M. (2020) · Clinical Neurophysiology · doi:10.1016/j.clinph.2019.09.021 · Cited by 68
Zhou, Y., Habibollahi, Z., Ibrahim, A., Jenkins, M. E., Naish, M. D., & Trejos, A. L. (2022) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2022.3211450 · Cited by 8
Metadata
- Authors
- Chisci, L., Mavino, A., Perferi, G., Sciandrone, M., Anile, C., Colicchio, G., & Fuggetta, F.
- Venue
- IEEE Transactions on Biomedical Engineering
- Published
- 2010-05
- Volume
- 57
- Issue
- 5
- Pages
- 1124-1132
- ISSN
- 0018-9294 (ISSN-L)
- OpenAlex
- W2127261457
- Cited by
- 255
- References
- 1
- FWCI
- 6.34
- Type
- journal article
- Language
- en
- Open access
- closed
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- Chisci, L., Mavino, A., Perferi, G., Sciandrone, M., Anile, C., Colicchio, G., & Fuggetta, F. (2010). Real-Time Epileptic Seizure Prediction Using AR Models and Support Vector Machines. IEEE Transactions on Biomedical Engineering. https://doi.org/10.1109/tbme.2009.2038990
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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