CISUC

EPILEPSIAE - A European epilepsy database

Authors

Abstract

With a worldwide prevalence of about 1%, epilepsy is one of the most common serious brain diseases with profound physical, psychological and, social consequences. Characteristic symptoms are seizures caused by abnormally synchronized neuronal activity that can lead to temporary impairments of motor functions, perception, speech, memory or, consciousness.
The possibility to predict the occurrence of epileptic seizures by monitoring the electroencephalographic activity (EEG) is considered one of the most promising options to establish new therapeutic strategies for the considerable fraction of patients with currently insufficiently controlled seizures.
Here, a database is presented which is part of an EU-funded project 'EPILEPSIAE' aiming at the development of seizure prediction algorithms which can monitor the EEG for seizure precursors. High-quality, long-term continuous EEG data, enriched with clinical metadata, which so far have not been available, are managed in this database as a joint effort of epilepsy centers in Portugal (Coimbra), France (Paris) and Germany (Freiburg).
The architecture and the underlying schema are here reported for this database. It was designed for an efficient organization, access and search of the data of 300 epilepsy patients, including high quality long-term EEG recordings, obtained with scalp and intracranial electrodes, as well as derived features and supplementary clinical and imaging data. The organization of this European database will allow for accessibility by a wide spectrum of research groups and may serve as a model for similar databases planned for the future.

Keywords

Database; Schema; Epilepsy; Seizure prediction; EEG

Journal

Computer methods and programs in biomedicine, January 2012

Cited by

Year 2016 : 11 citations

 Gadhoumi, K., Lina, J.M., Mormann, F. and Gotman, J., 2016. Seizure prediction for therapeutic devices: A review. Journal of neuroscience methods, 260, pp.270-282.

 Birjandtalab, J., Pouyan, M.B. and Nourani, M., 2016, February. Nonlinear dimension reduction for eeg-based epileptic seizure detection. In Biomedical and Health Informatics (BHI), 2016 IEEE-EMBS International Conference on (pp. 595-598). IEEE.

 Behbahani, S., Dabanloo, N.J., Nasrabadi, A.M. and Dourado, A., 2016. Classification of ictal and seizure-free HRV signals with focus on lateralization of epilepsy. Technology and Health Care, 24(1), pp.43-56.

 Kini, L.G., Davis, K.A. and Wagenaar, J.B., 2016. Data integration: Combined imaging and electrophysiology data in the cloud. NeuroImage, 124, pp.1175-1181.

 Meisel, C., Plenz, D., Schulze?Bonhage, A. and Reichmann, H., 2016. Quantifying antiepileptic drug effects using intrinsic excitability measures. Epilepsia, 57(11).

 Villegas-Martínez, I., de-Miguel-Elízaga, I., Carrasco-Torres, R., Marras, C., Canteras-Jordana, M., Yedra-Guzmán, M.J., Martínez-Villanueva, M., Tortosa-Conesa, D. and Martín-Fernández, J., 2016. The COL1A1 SP1 polymorphism is associated with lower bone mineral density in patients treated with valproic acid. Pharmacogenetics and genomics, 26(3), pp.126-132.

 Qaraqe, M., Ismail, M. and Serpedin, E., 2016, August. Combined matching pursuit and Wigner-Ville Distribution analysis for the discrimination of ictal heart rate variability. In Signal Processing Conference (EUSIPCO), 2016 24th European (pp. 2045-2049). IEEE.

 Tsiouris, K.M., Tzallas, A.T., Markoula, S., Koutsouris, D., Konitsiotis, S. and Fotiadis, D.I., 2016. A Review of Automated Methodologies for the Detection of Epileptic Episodes Using Long-Term EEG Signals. In Handbook of Research on Trends in the Diagnosis and Treatment of Chronic Conditions (pp. 231-261). IGI Global.

 Qaraqe, M., Ismail, M., Serpedin, E. and Zulfi, H., 2016. Epileptic seizure onset detection based on EEG and ECG data fusion. Epilepsy & Behavior, 58, pp.48-60.

 Milanowski, P. and Suffczynski, P., 2016. Seizures Start without Common Signatures of Critical Transition. International Journal of Neural Systems, 26(08), p.1650053.

 Fathima, T., Joseph, P.K. and Bedeeuzzaman, M., 2016. Wavelet Packet Analysis for Automatic Seizure Detection and Latency Study in Scalp EEG. Journal of Medical Imaging and Health Informatics, 6(3), pp.681-687.

Year 2015 : 7 citations

 Meisel, Christian, et al. "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 (2015): 14694-14699.

 Wagenaar, Joost B., et al. "Collaborating and sharing data in epilepsy research." Journal of Clinical Neurophysiology 32.3 (2015): 235-239.

 Kini, Lohith G., Kathryn A. Davis, and Joost B. Wagenaar. "Data integration: Combined imaging and electrophysiology data in the cloud." NeuroImage 124 (2016): 1175-1181.

 Gadhoumi, Kais, et al. "Seizure prediction for therapeutic devices: a review." Journal of neuroscience methods (2015).

 Donos, Cristian, Matthias Dümpelmann, and Andreas Schulze-Bonhage. "Early Seizure detection Algorithm Based on Intracranial EEG and Random Forest Classification." International Journal of Neural Systems (2015).

 Zhang, J. L., et al. "Phase Average Waveform Analysis of Different Leads in Epileptic EEG Signals." Journal of Medical Imaging and Health Informatics 5.8 (2015): 1811-1815.

 Behbahani, Soroor, et al. "Classification of ictal and seizure-free HRV signals with focus on lateralization of epilepsy." Technology and Health Care Preprint (2015): 1-14.

Year 2014 : 4 citations

 Lee, Sang-Hong, et al. "Classification of normal and epileptic seizure EEG signals using wavelet transform, phase-space reconstruction, and Euclidean distance." Computer methods and programs in biomedicine 116.1 (2014): 10-25.

 Gómez-Gil, Pilar, et al. "Identification of Epilepsy Seizures Using Multi-resolution Analysis and Artificial Neural Networks." Recent Advances on Hybrid Approaches for Designing Intelligent Systems. Springer International Publishing, 2014. 337-351.

 Biswas, R.; Khamaru, K.; Majumdar, K.K., "A Peak Synchronization Measure for Multiple Signals," Signal Processing, IEEE Transactions on , vol.62, no.17, pp.4390,4398, Sept.1, 2014

 Azinfar, Leila, et al. "Optimizing dynamical similarity index extraction window for seizure detection." Engineering in Medicine and Biology Society (EMBC), 2014 36th Annual International Conference of the IEEE. IEEE, 2014.

Year 2013 : 3 citations

 Cockfield, Jeremy, Kyungmin Su, and Kay A. Robbins. "MOBBED: a computational data infrastructure for handling large collections of event-rich time series datasets in MATLAB." Frontiers in neuroinformatics 7 (2013).

 Afadiyanti, Alfi. Analisis Mutasi Pasien Mental Retardasi dengan Epilepsi di Indonesia. Diss. Diponegoro University, 2013.

 Alam, S., and M. Bhuiyan. "Detection of Seizure and Epilepsy using Higher-order Statistics in the EMD Domain." (2013): 1-1.

Year 2012 : 5 citations

 Meisel, C., Storch, A., Hallmeyer-Elgner, S., Bullmore, E., & Gross, T. (2012). Failure of adaptive self-organized criticality during epileptic seizure attacks. PLoS computational biology, 8(1), e1002312.

 Burch, J., Marson, A., Beyer, F., Soares, M., Hinde, S., Wieshmann, U., and Woolacott, N. Dilemmas in the interpretation of diagnostic accuracy studies on presurgical workup for epilepsy surgery. Epilepsia 53, 8 (2012), 1294–1302.

 Dickinson, P., and Looper, K. J. Psychogenic nonepileptic seizures: A current overview. Epilepsia 53, 10 (2012), 1679–1689.

 Williamson, J. R., Bliss, D. W., Browne, D. W., and Narayanan, J. T. Seizure prediction using eeg spatiotemporal correlation structure. Epilepsy & Behavior 25, 2 (10 2012), 230–238.

 Meisel, C., and Kuehn, C. Scaling effects and spatio-temporal multilevel dynamics in epileptic seizures. PLoS ONE 7, 2 (02 2012), e30371 EP –.

Year 2011 : 3 citations

 Meisel, Christian, and Christian Kuehn. "Toward Multiscale Modeling and Prediction of Epileptic Seizures." arXiv preprint arXiv:1103.5934 (2011).

 Kuehn, Christian, and Christian Meisel. "On spatial and temporal multilevel dynamics and scaling effects in epileptic seizures." arXiv preprint arXiv:1103.5934 (2011).

 Alvarado-Rojas, C., et al. "Probing cortical excitability using cross-frequency coupling in intracranial EEG recordings: A new method for seizure prediction." Engineering in Medicine and Biology Society, EMBC, 2011 Annual International Conference of the IEEE. IEEE, 2011.