Abstract
We present in this study a novel approach to predicting EEG epileptic seizures: we accurately model and predict non-ictal cortical activity and use prediction errors as parameters that significantly distinguish ictal from non-ictal activity. We suppress seizure-related activity by modeling EEG signal acquisition as a cocktail party problem and obtaining seizure-related activity using Independent Component Analysis. Following recent studies intricately linking seizure to increased, widespread synchrony, we construct dynamic EEG synchronization graphs in which the electrodes are represented as nodes and the pair-wise correspondences between them are represented by edges. We extract 38 intuitive features from the synchronization graph as well as the original signal. From this, we use a rigorous method of feature selection to determine minimally redundant features that can describe the non-ictal EEG signal maximally. We learn a one-step forecast operator restricted to just these features, using autoregression (AR(1)). We improve this in a novel way by cross-learning common knowledge across patients and recordings using Transfer Learning, and devise a novel transformation to increase the efficiency of transfer learning. We declare imminent seizure based on detecting outliers in our prediction errors using a simple and intuitive method. Our median seizure detection time is 11.04 min prior to the labeled start of the seizure compared to a benchmark of 1.25 min prior, based on previous work on the topic. To the authors’ best knowledge this is the first attempt to model seizure prediction in this manner, employing efficient seizure suppression, the use of synchronization graphs and transfer learning, among other novel applications.
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References
Acar, E., Aykut-Bingol, C., Bingol, H., Bro, R., Yener, B.: Multiway analysis of epilepsy tensors. Bioinformatics 23(13), i10–i18 (2007)
Alkan, A., Koklukaya, E., Subasi, A.: Automatic seizure detection in EEG using logistic regression and artificial neural network. J. Neurosci. Meth. 148(2), 167–176 (2005)
Anderson, N.R., Wisneski, K., Eisenman, L., Moran, D.W., Leuthardt, E.C., Krusienski, D.J.: An offline evaluation of the autoregressive spectrum for electrocorticography. IEEE Trans. Biomed. Eng. 56(3), 913–916 (2009)
Barrat, A., Barthelemy, M., Vespignani, A.: Dynamical Processes on Complex Networks, vol. 1. Cambridge University Press, Cambridge (2008)
Bertsekas, D.P.: Nonlinear Programming, 2nd edn. Athena Scientific, Belmont (1999)
Bilgin, C.C., Ray, S., Baydil, B., Daley, W.P., Larsen, M., Yener, B.: Multiscale feature analysis of salivary gland branching morphogenesis. PLoS ONE 7(3), e32906 (2012)
Boccaletti, S., Latora, V., Moreno, Y., Chavez, M., Hwang, D.U.: Complex networks: structure and dynamics. Phys. Rep. 424(4–5), 175–308 (2006). http://www.sciencedirect.com/science/article/pii/S037015730500462X
Bronzino, J.D.: Principles of electroencephalography. In: Biomedical Engineering Handbook, 3rd edn. Taylor and Francis, New York (2006)
Bullmore, E., Sporns, O.: Complex brain networks: graph theoretical analysis of structural and functional systems. Nat. Rev. Neurosci. 10(3), 186–198 (2009)
Chandaka, S., Chatterjee, A., Munshi, S.: Cross-correlation aided support vector machine classifier for classification of EEG signals. Expert Syst. Appl. 36(2 Part 1), 1329–1336 (2009)
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)
Comon, P.: Independent component analysis - a new concept. Signal Process. 36, 287–314 (1994)
Comon, P., Jutten, C.: Handbook of Blind Source Separation: Independent Component Analysis and Applications, 1st edn. Academic Press, Oxford (2010)
Corsini, J., Shoker, L., Sanei, S., Alarcon, G.: Epileptic seizure predictability from scalp EEG incorporating constrained blind source separation. IEEE Trans. Biomed. Eng. 53, 790–799 (2006)
Cranstoun, S.D., Ombao, H.C., von Sachs, R., Guo, W., Litt, B., et al.: Time-frequency spectral estimation of multichannel EEG using the auto-slex method. IEEE Trans. Biomed. Eng. 49, 988–996 (2002)
D’Alessandro, M., Vachtsevanos, G., Esteller, R., Echauz, J., Cranstoun, S., Worrell, G., et al.: A multi-feature and multi-channel univariate selection process for seizure prediction. Clin. Neurophysiol. 116, 506–516 (2005)
Delorme, A., Sejnowski, T.J., Makeig, S.: Enhanced detection of artifacts in EEG data using higher-order statistics and independent component analysis. Neuroimage 34(4), 1443–1449 (2007)
Demir, C., Gultekin, S.H., Yener, B.: Augmented cell-graphs for automated cancer diagnosis. Bioinformatics 21(Suppl. 2), ii7–ii12 (2005)
Dhulekar, N., Oztan, B., Yener, B., Bingol, H.O., Irim, G., Aktekin, B., Aykut-Bingol, C.: Graph-theoretic analysis of epileptic seizures on scalp EEG recordings. In: Proceedings of the 5th ACM Conference on Bioinformatics, Computational Biology, and Health Informatics, BCB 2014, pp. 155–163. ACM, New York (2014). http://doi.acm.org/10.1145/2649387.2649423
Douw, L., van Dellen, E., de Groot, M., Heimans, J.J., Klein, M., Stam, C.J., Reijneveld, J.C.: Epilepsy is related to theta band brain connectivity and network topology in brain tumor patients. BMC Neurosci. 11(1), 103 (2010)
Elger, C.E.: Future trends in epileptology. Curr. Opin. Neurol. 14, 185–186 (2001)
Esteller, R., Echauz, J., D’Alessandro, M., Worrell, G., Cranstoun, S., Vachtsevanos, G., et al.: Continuous energy variation during the seizure cycle: towards an on-line accumulated energy. Clin. Neurophysiol. 116, 517–526 (2005)
Fisher, N., Talathi, S.S., Carney, P.R., Ditto, W.L.: Epilepsy detection and monitoring. In: Tong, S., Thankor, N.V. (eds.) Quantitative EEG Analysis Methods and Applications, pp. 157–183. Artech House (2008)
Fisher, R.S., van Emde Boas, W., Blume, W., Elger, C., Genton, P., Lee, P., Engel, J.J.: Epileptic seizures and epilepsy: definitions proposed by the international league against epilepsy (ILAE) and the international bureau for epilepsy (IBE). Epilepsia 46(4), 470–472 (2005)
Giannakakis, G., Sakkalis, V., Pediaditis, M., Tsiknakis, M.: Methods for seizure detection and prediction: an overview. Neuromethods, 1–27 (2014)
Güler, N.F., Übeyli, E.D., Güler, I.: Recurrent neural networks employing lyapunov exponents for EEG signals classification. Expert Syst. Appl. 29(3), 506–514 (2005)
Harrison, M.A., Frei, M.G., Osorio, I.: Accumulated energy revisited. Clin. Neurophysiol. 116, 527–531 (2005a)
Hazarika, N., Chen, J.Z., Tsoi, A.C., Sergejew, A.: Classification of EEG signals using the wavelet transform. Signal Process. 59, 61–72 (1997)
Iasemidis, L.D., Shiau, D.S., Pardalos, P.M., Chaovalitwongse, W., Narayanan, K., Prasad, A., et al.: Long-term prospective on-line real-time seizure-prediction. Clin. Neurophysiol. 116, 532–544 (2005)
Jasper, H.H.: The ten-twenty electrode system of the international federation. Electroencephalogr Clin. Neurophysiol. Suppl. 10, 371–375 (1958)
Jouny, C.C., Franaszczuk, P.J., Bergey, G.K.: Signal complexity and synchrony of epileptic seizures: is there an identifiable preictal period? Clin. Neurophysiol. 116, 552–558 (2005)
Jung, T.P., Makeig, S., Humphries, C., Lee, T.W., McKeown, M.J., Iragui, V., Sejnowski, T.J.: Removing electroencephalographic artifacts by blind source separation. Psychophysiology 37, 163–178 (2000)
Jutten, C., Herault, J.: Blind separation of sources, part I: an adaptive algorithm based on neuromimetic architecture. Signal Process. 24, 1–10 (1991)
Kannathal, N., Choo, M.L., Rajendra Acharya, U., Sadasivan, P.K.: Entropies for detection of epilepsy in EEG. Comput. Meth. Programs Biomed. 80(3), 187–194 (2005)
Kramer, M.A., Kolaczyk, E.D., Kirsch, H.E.: Emergent network topology at seizure onset in humans. Epilepsy Res. 79(2), 173–186 (2008)
Kuhnert, M.T., Elger, C.E., Lehnertz, K.: Long-term variability of global statistical properties of epileptic brain networks. Chaos: Interdisc. J. Nonlinear Sci. 20(4), 043126 (2010). http://scitation.aip.org/content/aip/journal/chaos/20/4/10.1063/1.3504998
Le Van, Q.M., Navarro, V., Martinerie, J., Baulac, M., Varela, F.J.: Toward a neurodynamical understanding of ictogenesis. Epilepsia 44(12), 30–43 (2003)
Le Van, Q.M., Soss, J., Navarro, V., Robertson, R., Chavez, M., Baulac, M., Martinerie, J.: Preictal state identification by synchronization changes in long-term intracranial EEG recordings. Clin. Neurophysiol. 116, 559–568 (2005)
Le Van Quyen, M., Soss, J., Navarro, V., Robertson, R., Chavez, M., Baulac, M., et al.: Preictal state identification by synchronization changes in long-term intracranial EEG recordings. Clin. Neurophysiol. 116, 559–568 (2005)
Lee, T.W., Girolami, M., Sejnowski, T.J.: Independent component analysis using an extended infomax algorithm for mixed sub-gaussian and super-gaussian sources. Neural Comput. 11(2), 417–441 (1999)
Lehnertz, K., Litt, B.: The first international collaborative workshop on seizure prediction: summary and data description. Clin. Neurophysiol. 116, 493–505 (2005)
Li, G., Semerci, M., Yener, B., Zaki, M.J.: Effective graph classification based on topological and label attributes. Stat. Anal. Data Min. ASA Data Sci. J. 5(4), 265–283 (2012)
Litt, B., Esteller, R., Echauz, J., D’Alessandro, M., Shor, R., et al.: Epileptic seizures may begin hours in advance of clinical onset: a report of five patients. Neuron 30, 51–64 (2001)
Liu, H.S., Zhang, T., Yang, F.S.: A multistage, multimethod approach for automatic detection and classification of epileptiform EEG. IEEE Trans. Biomed. Eng. 49(12 Pt 2), 1557–1566 (2002)
Lytton, W.W.: Computer modeling of Epilepsy. Nat. Rev. Neurosci. 9(8), 626–637 (2008)
Mahyari, A., Aviyente, S.: Identification of dynamic functional brain network states through tensor decomposition. In: 39th IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2014) (2014)
Mirowski, P., Madhavan, D., LeCun, Y., Kuzniecky, R.: Classification of patterns of EEG synchronization for seizure prediction. Clin. Neurophysiol. 120, 1927–1940 (2009)
Mormann, F., Andrzejak, R.G., Elger, C.E., Lehnertz, K.: Seizure prediction: the long and winding road. Brain 130, 314–333 (2007)
Mormann, F., Kreuz, T., Andrzejak, R., David, P., Lehnertz, K., et al.: Epileptic seizures are preceded by a decrease in synchronization. Epilepsy Res. 53, 173–185 (2003)
Mormann, F., Kreuz, T., Rieke, C., Andrzejak, R.G., Kraskov, A., David, P., et al.: On the predictability of epileptic seizures. Clin. Neurophysiol. 116, 569–587 (2005)
Mormann, F., Lehnertz, K., David, P., Elger, C.E.: Mean phase coherence as measure for phase synchronization and its application to the EEG of epilepsy patients. Physica D 144, 358–369 (2000)
Murali, S., Kulish, V.V.: Modeling of evoked potentials of electroencephalograms: an overview. Digit. Signal Process. 17, 665–674 (2007)
Muthuswamy, J., Thakor, N.V.: Spectral analysis methods for neurological signals. J. Neurosci. Meth. 83, 1–14 (1998)
Newman, M.E.J.: The structure and function of complex networks. SIAM Rev. 45, 167–256 (2003)
Osorio, I., Zaveri, H., Frei, M., Arthurs, S.: Epilepsy: The Intersection of Neurosciences, Biology, Mathematics, Engineering, and Physics. Taylor & Francis (2011). http://books.google.com/books?id=O97hKvyyYgsC
Pan, S.J., Yang, Q.: A survey on transfer learning. IEEE Trans. Knowl. Data Eng. 22(10), 1345–1359 (2010)
van Putten, M.J.A.M., Kind, T., Visser, F., Lagerburg, V.: Detecting temporal lobe seizures from scalp EEG recordings: a comparison of various features. Clin. Neurophysiol. 116(10), 2480–2489 (2005)
Rodriguez-Lujan, I., Huerta, R., Elkan, C., Cruz, C.S.: Quadratic programming feature selection. J. Mach. Learn. Res. 11, 1491–1516 (2010)
Rogowski, Z., Gath, I., Bental, E.: On the prediction of epileptic seizures. Biol. Cybern. 42, 9–15 (1981)
Salant, Y., Gath, I., Henriksen, O.: Prediction of epileptic seizures from two-channel EEG. Med. Biol. Eng. Comput. 36, 549–556 (1998)
Schindler, K.A., Bialonski, S., Horstmann, M.T., Elger, C.E., Lehnertz, K.: Evolving functional network properties and synchronizability during human epileptic seizures. CHAOS: Interdisc. J. Nonlinear Sci. 18(3), 033119 (2008)
Siegel, A., Grady, C.L., Mirsky, A.F.: Prediction of spike-wave bursts in absence epilepsy by EEG power-spectrum signals. Epilepsia 116, 2266–2301 (1982)
Smith, S.J.M.: EEG in the diagnosis, classification, and management of patients with epilepsy. J. Neurol. Neurosurg. Psychiatry 76, ii2–ii7 (2005)
Srinivasan, V., Eswaran, C., Sriraam, N.: Artificial neural network based epileptic detection using time-domain and frequency-domain features. J. Med. Syst. 29(6), 647–660 (2005)
Stam, C.J., Nolte, G., Daffertshofer, A.: Phase lag index: assessment of functional connectivity from multi channel EEG and MEG with diminished bias from common sources. Hum. Brain Mapp. 28, 1178–1193 (2007)
Stam, C., van Straaten, E.: The organization of physiological brain networks. Clin. Neurophysiol. 123, 1067–1087 (2012)
Strogatz, S.H.: Exploring complex networks. Nature 410(6825), 268–276 (2001). http://dx.doi.org/10.1038/35065725
Subasi, A., Alkan, A., Koklukaya, E., Kiymik, M.K.: Wavelet neural network classification of EEG signals by using ar models with mle processing. Neural Netw. 18(7), 985–997 (2005)
Tzallas, A.T., Tsipouras, M.G., Fotiadis, D.I.: The use of time-frequency distributions for epileptic seizure detection in EEG recordings. In: 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 1265–1268 (2007)
Tzallas, A.T., Tsipouras, M.G., Fotiadis, D.I.: Epileptic seizure detection in EEGs using time-frequency analysis. IEEE Trans. Inf. Technol. Biomed. 13(5), 703–710 (2009)
Viglione, S.S., Walsh, G.O.: Epileptic seizure prediction. Electroencephalogr. Clin. Neurophysiol. 39, 435–436 (1975)
Wang, C., Mahadevan, S.: Manifold alignment using procrustes analysis. In: Proceedings of the 25th International Conference on Machine Learning, ICML 2008, pp. 1120–1127. ACM, New York (2008). http://doi.acm.org/10.1145/1390156.1390297
Wu, H., Li, X., Guan, X.: Networking property during epileptic seizure with multi-channel EEG recordings. In: Wang, J., Yi, Z., Żurada, J.M., Lu, B.-L., Yin, H. (eds.) ISNN 2006. LNCS, vol. 3973, pp. 573–578. Springer, Heidelberg (2006). http://dx.doi.org/10.1007/11760191_84
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Dhulekar, N., Nambirajan, S., Oztan, B., Yener, B. (2015). Seizure Prediction by Graph Mining, Transfer Learning, and Transformation Learning. In: Perner, P. (eds) Machine Learning and Data Mining in Pattern Recognition. MLDM 2015. Lecture Notes in Computer Science(), vol 9166. Springer, Cham. https://doi.org/10.1007/978-3-319-21024-7_3
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