Automated noninvasive seizure detection and localization using switching markov models and convolutional neural networks

Jeff Craley, Emily Johnson, Christophe Jouny, Archana Venkataraman

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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.

Original languageEnglish (US)
Title of host publicationMedical Image Computing and Computer Assisted Intervention – MICCAI 2019 - 22nd International Conference, Proceedings
EditorsDinggang Shen, Pew-Thian Yap, Tianming Liu, Terry M. Peters, Ali Khan, Lawrence H. Staib, Caroline Essert, Sean Zhou
PublisherSpringer
Pages253-261
Number of pages9
ISBN (Print)9783030322502
DOIs
StatePublished - Jan 1 2019
Event22nd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2019 - Shenzhen, China
Duration: Oct 13 2019Oct 17 2019

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11767 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference22nd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2019
CountryChina
CityShenzhen
Period10/13/1910/17/19

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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  • Cite this

    Craley, J., Johnson, E., Jouny, C., & Venkataraman, A. (2019). Automated noninvasive seizure detection and localization using switching markov models and convolutional neural networks. In D. Shen, P-T. Yap, T. Liu, T. M. Peters, A. Khan, L. H. Staib, C. Essert, & S. Zhou (Eds.), Medical Image Computing and Computer Assisted Intervention – MICCAI 2019 - 22nd International Conference, Proceedings (pp. 253-261). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11767 LNCS). Springer. https://doi.org/10.1007/978-3-030-32251-9_28