EPILEPSIAE Database

Publications


  1. Lopes F, Leal A, Medeiros J, Pinto MF, Dourado A, Dümpelmann M, Teixeira C. EPIC: Annotated epileptic EEG independent components for artifact reduction. Scientific Data (2022) 9:Article number: 512.

  2. Yang, Y. et al. (2022) ‘A multimodal AI system for out-of-distribution generalization of seizure identification’, IEEE Journal of Biomedical and Health Informatics. IEEE, 2194(c), pp. 1–10. doi: 10.1109/JBHI.2022.3157877.

  3. Baghersalimi, S. et al. (2022) ‘Personalized Real-Time Federated Learning for Epileptic Seizure Detection’, IEEE Journal of Biomedical and Health Informatics. IEEE, 26(2), pp. 898–909. doi: 10.1109/JBHI.2021.3096127.

  4. Sopic, D. et al. (2022) ‘Personalized seizure signature: An interpretable approach to false alarm reduction for long‐term epileptic seizure detection’, Epilepsia, (January), pp. 1–11. doi: 10.1111/epi.17176.

  5. Sanz-García, A., Perez-Romero, M. and Ortega, G. J. (2022) ‘Spectral and network characterization of focal seizure types and phases’, Computer Methods and Programs in Biomedicine. Elsevier B.V., p. 106704. doi: 10.1016/j.cmpb.2022.106704.

  6. Lopes, F. et al. (2022) ‘Ensemble Deep Neural Network for Automatic Classification of EEG Independent Components’, IEEE Transactions on Neural Systems and Rehabilitation Engineering, 30, pp. 1–1. doi: 10.1109/tnsre.2022.3154891.

  7. Pinto, M. et al. (2022) ‘Interpretable EEG seizure prediction using a multiobjective evolutionary algorithm’, Scientific Reports. Nature Publishing Group UK, (0123456789), pp. 1–15. doi: 10.1038/s41598-022-08322-w.

  8. Leal, A. et al. (2021) ‘Heart rate variability analysis for the identification of the preictal interval in patients with drug-resistant epilepsy’, Scientific Reports. Nature Publishing Group UK, 11(1), pp. 1–11. doi: 10.1038/s41598-021-85350-y.

  9. Lopes, F. et al. (2021) ‘Automatic Electroencephalogram Artifact Removal Using Deep Convolutional Neural Networks’, IEEE Access, 9, pp. 149955–149970. doi: 10.1109/ACCESS.2021.3125728.

  10. Vandecasteele, K. et al. (2021) ‘The power of ECG in multimodal patient-specific seizure monitoring: Added value to an EEG‐based detector using limited channels’, Epilepsia, (January), p. epi.16990. doi: 10.1111/epi.16990.

  11. Pinto, M. F. et al. (2021) ‘A personalized and evolutionary algorithm for interpretable EEG epilepsy seizure prediction’, Scientific Reports. Nature Publishing Group UK, 11(1), p. 3415. doi: 10.1038/s41598-021-82828-7.

  12. Liu, T. et al. (2020) ‘Epileptic Seizure Classification With Symmetric and Hybrid Bilinear Models’, IEEE Journal of Biomedical and Health Informatics. IEEE, 24(10), pp. 2844–2851. doi: 10.1109/JBHI.2020.2984128.

  13. Gómez, C. et al. (2020) ‘Automatic seizure detection based on imaged-EEG signals through fully convolutional networks’, Scientific Reports. Nature Publishing Group UK, 10(1), p. 21833. doi: 10.1038/s41598-020-78784-3.

  14. Stojanović, O., Kuhlmann, L. and Pipa, G. (2020) ‘Predicting epileptic seizures using nonnegative matrix factorization’, PLOS ONE. Edited by L. M. Ward, 15(2), p. e0228025. doi: 10.1371/journal.pone.0228025.

  15. Saggio, M. L. et al. (2020) ‘A taxonomy of seizure dynamotypes’, eLife, 9, pp. 1–56. doi: 10.7554/eLife.55632.

  16. Heller, S. et al. (2018) ‘Hardware Implementation of a Performance and Energy-optimized Convolutional Neural Network for Seizure Detection’, in 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 2268–2271. doi: 10.1109/EMBC.2018.8512735.

  17. Manzouri, F. et al. (2018) ‘A Comparison of Machine Learning Classifiers for Energy-Efficient Implementation of Seizure Detection’, Frontiers in Systems Neuroscience, 12(September), pp. 1–11. doi: 10.3389/fnsys.2018.00043.

  18. Hügle, M. et al. (2018) ‘Early Seizure Detection with an Energy-Efficient Convolutional Neural Network on an Implantable Microcontroller’, in 2018 International Joint Conference on Neural Networks (IJCNN). Rio de Janeiro: IEEE, pp. 1–7. doi: 10.1109/IJCNN.2018.848949.

  19. Donos, C. et al. (2018) ‘Seizure onset predicts its type’, Epilepsia, 59(December 2017), pp. 650–660. doi: 10.1111/epi.13997.

  20. Manzouri, F. et al. (2017) ‘Optimized detector for closed-loop devices for neurostimulation’, in 2017 IEEE International Conference on Systems, Man, and Cybernetics (SMC). IEEE, pp. 2158–2163. doi: 10.1109/SMC.2017.8122939.

  21. Ferastraoaru, V. et al. (2016) ‘Termination of seizure clusters is related to the duration of focal seizures’, Epilepsia, 57(6), pp. 889–895. doi: 10.1111/epi.13375.

  22. Meisel, C. et al. (2016) ‘Quantifying antiepileptic drug effects using intrinsic excitability measures’, Epilepsia, 57(11), pp. e210–e215. doi: 10.1111/epi.13517.

  23. Qaraqe, M. et al. (2016) ‘Epileptic seizure onset detection based on EEG and ECG data fusion’, Epilepsy & Behavior, 58, pp. 48–60. doi: 10.1016/j.yebeh.2016.02.039.

  24. Direito, B. et al. (2016) ‘A Realistic Seizure Prediction Study Based on Multiclass SVM’, International Journal of Neural Systems, 27(3), p. 1750006. doi: 10.1142/S012906571750006X.

  25. Donos, C., Dümpelmann, M. and Schulze-Bonhage, A. (2015) ‘Early Seizure Detection Algorithm Based on Intracranial EEG and Random Forest Classification.’, International journal of neural systems, 25(5), p. 1550023. doi: 10.1142/S0129065715500239.

  26. Alvarado-Rojas, C. et al. (2015) ‘Slow modulations of high-frequency activity (40–140 Hz) discriminate preictal changes in human focal epilepsy’, Scientific Reports, 4(1), p. 4545. doi: 10.1038/srep04545.

  27. Meisel, C. et al. (2015) ‘Intrinsic excitability measures track antiepileptic drug action and uncover increasing/decreasing excitability over the wake/sleep cycle’, Proceedings of the National Academy of Sciences, 112(47), pp. 14694–14699. doi: 10.1073/pnas.1513716112.

  28. Bandarabadi, M. et al. (2014) ‘Sub-band Mean Phase Coherence for Automated Epileptic Seizure Detection’, in IFMBE Proceedings, pp. 319–322. doi: 10.1007/978-3-319-03005-0.

  29. Bandarabadi, M. et al. (2014) ‘Robust and low complexity algorithms for seizure detection’, in 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE, pp. 4447–4450. doi: 10.1109/EMBC.2014.6944611.

  30. Alexandre Teixeira, C. et al. (2014) ‘Epileptic seizure predictors based on computational intelligence techniques: A comparative study with 278 patients’, Computer Methods and Programs in Biomedicine. Elsevier Ireland Ltd, 114(3), pp. 324–336. doi: 10.1016/j.cmpb.2014.02.007.

  31. Klatt, J. et al. (2012) ‘The EPILEPSIAE database: An extensive electroencephalography database of epilepsy patients’, Epilepsia, 53(9), pp. 1669–1676. doi: 10.1111/j.1528-1167.2012.03564.x.

  32. Ihle, M. et al. (2012) ‘EPILEPSIAE – A European epilepsy database’, Computer Methods and Programs in Biomedicine. Elsevier Ireland Ltd, 106(3), pp. 127–138. doi: 10.1016/j.cmpb.2010.08.011.

  33. Teixeira, C. A. et al. (2011) ‘EPILAB: A software package for studies on the prediction of epileptic seizures’, Journal of Neuroscience Methods. Elsevier B.V., 200(2), pp. 257–271. doi: 10.1016/j.jneumeth.2011.07.002.

  34. Feldwisch-Drentrup, H. et al. (2011) ‘Anticipating the unobserved: Prediction of subclinical seizures’, Epilepsy and Behavior. Elsevier Inc., 22(SUPPL. 1), pp. S119–S126. doi: 10.1016/j.yebeh.2011.08.023.

  35. This list may miss some publications using data from the EPILEPSIAE database. Please send information on missing publications to:

    matthias.duempelmann(at)uniklinik-freiburg.de