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Brainatic: A System for Real-Time Epileptic Seizure Prediction

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Brain-Computer Interface Research

Abstract

A new system developed for real-time scalp EEG-based epileptic seizure prediction is presented, based on real time classification by machine learning methods, and named Brainatic. The system enables the consideration of previously trained classifiers for real-time seizure prediction. The software facilitates the computation of 22 univariate measures (features) per electrode, and classification using support vector machines (SVM), multilayer perceptron (MLP) neural networks and radial basis functions (RBF) neural networks. Brainatic was able to operate in real-time on a dual Intel® AtomTM netbook with 2GB of RAM, and was used to perform the clinical and ambulatory tests of the EU project EPILEPSIAE.

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References

  1. Kwan, P., Brodie, M.J.: Early identification of refractory epilepsy. N. Engl. J. Med. 342(5), 314–319 (2000)

    Article  Google Scholar 

  2. Cockerell, O.C., Hart, Y.M., Sander, J.W.A.S., Goodridge, D.M.G., Shorvon, S.D., Johnson, A.L.: Mortality from epilepsy: results from a prospective population-based study. The Lancet 344(8927), 918–921 (1994)

    Article  Google Scholar 

  3. Schulze-Bonhage, A., Sales, F., Wagner, K., Teotonio, R., Carius, A., Schelle, A., Ihle, M.: Views of patients with epilepsy on seizure prediction devices. Epilepsy and Behavior 18(4), 388–396 (2010)

    Article  Google Scholar 

  4. Mormann, F., Andrzejak, R.G., Elger, C.E., Lehnertz, K.: Seizure prediction: the long and winding road. Brain 130(2), 314–333 (2007)

    Article  Google Scholar 

  5. Schelter, B., Winterhalder, M., Maiwald, T., Brandt, A., Schad, A., Schulze-Bonhage, A., Timmer, J.: Testing statistical significance of multivariate time series analysis techniques for epileptic seizure prediction. Chaos: An Interdisciplinary Journal of Nonlinear Science 16(1), 013108 (2006)

    Article  Google Scholar 

  6. Schelter, B., Winterhalder, M., Maiwald, T., Brandt, A., Schad, A., Timmer, J., Schulze-Bonhage, A.: Do false predictions of seizures depend on the state of vigilance? a report from two seizure-prediction methods and proposed remedies. Epilepsia 47(12), 2058–2070 (2006)

    Article  Google Scholar 

  7. Dourado, A., Martins, R., Duarte, J., Direito, B.: Towards personalized neural networks for epileptic seizure prediction. In: Kůrková, V., Neruda, R., Koutník, J. (eds.) ICANN 2008, Part II. LNCS, vol. 5164, pp. 479–487. Springer, Heidelberg (2008)

    Chapter  Google Scholar 

  8. Costa, R.P., Oliveira, P., Rodrigues, G., Leitão, B., Dourado, A.: Epileptic seizure classification using neural networks with 14 features. In: Lovrek, I., Howlett, R.J., Jain, L.C. (eds.) KES 2008, Part II. LNCS (LNAI), vol. 5178, pp. 281–288. Springer, Heidelberg (2008)

    Chapter  Google Scholar 

  9. Mirowski, P., LeCun, Y., Madhavan, D., Kuzniecky, R.: Comparing SVM and convolutional networks for epileptic seizure prediction from intracranial EEG. In: IEEE Workshop on Machine Learning for Signal Processing, MLSP 2008, pp. 244–249 (October 2008)

    Google Scholar 

  10. Chisci, L., Mavino, A., Perferi, G., Sciandrone, M., Anile, C., Colicchio, G., Fuggetta, F.: Real-time epileptic seizure prediction using AR models and support vector machines. IEEE Trans. Biomed. Eng. 57(5), 1124–1132 (2010)

    Article  Google Scholar 

  11. Cortes, C., Vapnik, V.: Support-vector networks. Mach. Learn. 20, 273–297 (1995)

    MATH  Google Scholar 

  12. Castellaro, C., Favaro, G., Salemi, G., Sarto, M., Rizzo, N.: Hardware for seizure prediction: Towards wearable devices to support epileptic people. In: 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC, August 30-September 3, pp. 1628–1631 (2011)

    Google Scholar 

  13. Teixeira, C., Direito, B., Feldwisch-Drentrup, H., Valderrama, M., Costa, R., Alvarado-Rojas, C., Nikolopoulos, S., Quyen, M.L.V., Timmer, J., Schelter, B., Dourado, A.: Epilab: A software package for studies on the prediction of epileptic seizures. Journal of Neuroscience Methods 200(2), 257–271 (2011)

    Article  Google Scholar 

  14. Aarabi, A., Fazel-Rezai, R., Aghakhani, Y.: EEG seizure prediction: Measures and challenges. In: Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2009, pp. 1864–1867 (September 2009)

    Google Scholar 

  15. Rajdev, P., Ward, M., Rickus, J., Worth, R., Irazoqui, P.: Real-time seizure prediction from local field potentials using an adaptive Wiener algorithm. Comput. Biol. Med. 40(1), 97–108 (2010)

    Article  Google Scholar 

  16. Mormann, F., Kreuz, T., Rieke, C., Andrzejak, R.G., Kraskov, A., David, P., Elger, C.E., Lehnertz, K.: On the predictability of epileptic seizures. Clin. Neurophysiol. 116(3), 569–587 (2005)

    Article  Google Scholar 

  17. Hjorth, B.: EEG analysis based on time domain properties. Electroencephalogr. Clin. Neurophysiol. 29(3), 306–310 (1970)

    Article  Google Scholar 

  18. Mallat, S.: A theory for multiresolution signal decomposition: the wavelet representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 11(7), 674–693 (1989)

    Article  MATH  Google Scholar 

  19. Daubechies, I.: Ten lectures on wavelets. In: CBMS-NSF Conference Series in Applied Mathematics, SIAM (1992)

    Google Scholar 

  20. Feldwisch-Drentrup, H., Schelter, B., Jachan, M., Nawrath, J., Timmer, J., Schulze-Bonhage, A.: Joining the benefits: Combining epileptic seizure prediction methods. Epilepsia 51(8), 1598–1606 (2010)

    Article  Google Scholar 

  21. Schelter, B., Feldwisch-Drentrup, H., Ihle, M., Schulze-Bonhage, A., Timmer, J.: Seizure prediction in epilepsy: From circadian concepts via probabilistic forecasting to statistical evaluation. In: 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC, August 30-September 3, pp. 1624–1627 (2011)

    Google Scholar 

  22. Teixeira, C., Direito, B., Bandarabadi, M., Dourado, A.: Output regularization of svm seizure predictors: Kalman filter versus the “firing power” method. In: 2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), August 28-September 1, pp. 6530–6533 (2012)

    Google Scholar 

  23. Klatt, J., Feldwisch-Drentrup, H., Ihle, M., Navarro, V., Neufang, M., Teixeira, C., Adam, C., Valderrama, M., Alvarado-Rojas, C., Witon, A., Le Van Quyen, M., Sales, F., Dourado, A., Timmer, J., Schulze-Bonhage, A., Schelter, B.: The EPILEPSIAE database: an extensive electroencephalography database of epilepsy patients. Epilepsia 53(9), 1669–1676 (2012)

    Article  Google Scholar 

  24. Rasekhi, J., Mollaei, M.R., Bandarabadi, M., Teixeira, C.A., Dourado, A.: Preprocessing effects of 22 linear univariate features on the performance of seizure prediction methods. J. Neurosci. Methods 217(1-2), 9–16 (2013)

    Article  Google Scholar 

  25. Bandarabadi, M., Dourado, A., Teixeira, C.A., Netoff, T.I., Parhi, K.K.: Seizure prediction with bipolar spectral power features using Adaboost and SVM classifiers. In: Conf. Proc. IEEE Eng. Med. Biol. Soc., pp. 6305–6308 (2013)

    Google Scholar 

  26. Behbahani, S., Dabanloo, N.J., Nasrabadi, A.M., Teixseria, C.A., Dourado, A.: Pre-ictal heart rate variability assessment of epileptic seizures by means of linear and non-linear analyses (2013) [Epub ahead of print]

    Google Scholar 

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Correspondence to César Teixeira .

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Teixeira, C. et al. (2014). Brainatic: A System for Real-Time Epileptic Seizure Prediction. In: Guger, C., Allison, B., Leuthardt, E. (eds) Brain-Computer Interface Research. Biosystems & Biorobotics, vol 6. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-54707-2_2

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  • DOI: https://doi.org/10.1007/978-3-642-54707-2_2

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-54706-5

  • Online ISBN: 978-3-642-54707-2

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