TY - GEN
T1 - Automated noninvasive seizure detection and localization using switching markov models and convolutional neural networks
AU - Craley, Jeff
AU - Johnson, Emily
AU - Jouny, Christophe
AU - Venkataraman, Archana
N1 - Funding Information:
Acknowledgment. This work was supported by a JHMI Synergy Award (Venkatara-man/Johnson) and NSF CAREER 1845430 (Venkataraman).
Publisher Copyright:
© Springer Nature Switzerland AG 2019.
PY - 2019
Y1 - 2019
N2 - 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.
AB - 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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U2 - 10.1007/978-3-030-32251-9_28
DO - 10.1007/978-3-030-32251-9_28
M3 - Conference contribution
AN - SCOPUS:85075689623
SN - 9783030322502
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 253
EP - 261
BT - Medical Image Computing and Computer Assisted Intervention – MICCAI 2019 - 22nd International Conference, Proceedings
A2 - Shen, Dinggang
A2 - Yap, Pew-Thian
A2 - Liu, Tianming
A2 - Peters, Terry M.
A2 - Khan, Ali
A2 - Staib, Lawrence H.
A2 - Essert, Caroline
A2 - Zhou, Sean
PB - Springer Science and Business Media Deutschland GmbH
T2 - 22nd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2019
Y2 - 13 October 2019 through 17 October 2019
ER -