Abstract
We introduce a novel switching Markov model for combined epileptic seizure detection and localization from scalp electroencephalography (EEG). Using a hierarchy of Markov chains to fuse multichannel information, our model detects seizure onset, localizes the seizure focus, and tracks seizure activity as it spreads across the cortex. This model-based seizure tracking and localization is complemented by a nonparametric EEG likelihood using convolutional neural networks. We learn our model with an expectation-maximization algorithm that uses loopy belief propagation for approximate inference. We validate our model using leave one patient out cross validation on EEG acquired from two hospitals. Detection is evaluated on the publicly available Children’s Hospital Boston dataset. We validate both the detection and localization performance on a focal epilepsy dataset collected at Johns Hopkins Hospital. To the best of our knowledge, our model is the first to perform automated localization from scalp EEG across a heterogeneous patient cohort.
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References
Craley, J., Johnson, E., Venkataraman, A.: A novel method for epileptic seizure detection using coupled hidden markov models. In: Frangi, A.F., Schnabel, J.A., Davatzikos, C., Alberola-López, C., Fichtinger, G. (eds.) MICCAI 2018. LNCS, vol. 11072, pp. 482–489. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-00931-1_55
Craley, J., Johnson, E., Venkataraman, A.: Integrating convolutional neural networks and probabilistic graphical modeling for epileptic seizure detection in multichannel EEG. In: Chung, A.C.S., Gee, J.C., Yushkevich, P.A., Bao, S. (eds.) IPMI 2019. LNCS, vol. 11492, pp. 291–303. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-20351-1_22
French, J.A.: Refractory epilepsy: clinical overview. Epilepsia 48, 3–7 (2007)
Goldberger, A.L., et al.: Physiobank, physiotoolkit, and physionet: components of a new research resource for complex physiologic signals. Circulation 101(23), e215–e220 (2000)
Jurcak, V., et al.: 10/20, 10/10, and 10/5 systems revisited: their validity as relative head-surface-based positioning systems. Neuroimage 34(4), 1600–1611 (2007)
Kschischang, F.R., et al.: Factor graphs and the sum-product algorithm. IEEE Trans. Inf. Theory 47(2), 498–519 (2001)
Lüders, H.O., et al.: The epileptogenic zone: general principles. Epileptic Disord. 8(2), 1–9 (2006)
Plummer, C., Harvey, A.S., Cook, M.: EEG source localization in focal epilepsy: where are we now? Epilepsia 49(2), 201–218 (2008)
Vos, D., et al.: Canonical decomposition of ictal scalp eeg reliably detects the seizure onset zone. NeuroImage 37(3), 844–854 (2007)
Wilson, S.B., Emerson, R.: Spike detection: a review and comparison of algorithms. Clin. Neurophysiol. 113(12), 1873–1881 (2002)
Acknowledgment
This work was supported by a JHMI Synergy Award (Venkataraman/Johnson) and NSF CAREER 1845430 (Venkataraman).
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Craley, J., Johnson, E., Jouny, C., Venkataraman, A. (2019). Automated Noninvasive Seizure Detection and Localization Using Switching Markov Models and Convolutional Neural Networks. In: Shen, D., et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2019. MICCAI 2019. Lecture Notes in Computer Science(), vol 11767. Springer, Cham. https://doi.org/10.1007/978-3-030-32251-9_28
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DOI: https://doi.org/10.1007/978-3-030-32251-9_28
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