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2018 | OriginalPaper | Chapter

A Novel Method for Epileptic Seizure Detection Using Coupled Hidden Markov Models

Authors : Jeff Craley, Emily Johnson, Archana Venkataraman

Published in: Medical Image Computing and Computer Assisted Intervention – MICCAI 2018

Publisher: Springer International Publishing

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Abstract

We propose a novel Coupled Hidden Markov Model to detect epileptic seizures in multichannel electroencephalography (EEG) data. Our model defines a network of seizure propagation paths to capture both the temporal and spatial evolution of epileptic activity. To address the intractability introduced by the coupled interactions, we derive a variational inference procedure to efficiently infer the seizure evolution from spectral patterns in the EEG data. We validate our model on EEG aquired under clinical conditions in the Epilepsy Monitoring Unit of the Johns Hopkins Hospital. Using 5-fold cross validation, we demonstrate that our model outperforms three baseline approaches which rely on a classical detection framework. Our model also demonstrates the potential to localize seizure onset zones in focal epilepsy.

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Literature
2.
go back to reference Acharya, U.R., et al.: Automated diagnosis of epileptic EEG using entropies. Biomed. Signal Process. Control. 7(4), 401–408 (2012)CrossRef Acharya, U.R., et al.: Automated diagnosis of epileptic EEG using entropies. Biomed. Signal Process. Control. 7(4), 401–408 (2012)CrossRef
3.
go back to reference Güler, N.F., et al.: Recurrent neural networks employing Lyapunov exponents for EEG signals classification. Expert. Syst. Appl. 29(3), 506–514 (2005)CrossRef Güler, N.F., et al.: Recurrent neural networks employing Lyapunov exponents for EEG signals classification. Expert. Syst. Appl. 29(3), 506–514 (2005)CrossRef
4.
go back to reference Zandi, A.S., et al.: Automated real-time epileptic seizure detection in scalp EEG recordings using an algorithm based on wavelet packet transform. IEEE Trans. Biomed. Eng. 57(7), 1639–1651 (2010)CrossRef Zandi, A.S., et al.: Automated real-time epileptic seizure detection in scalp EEG recordings using an algorithm based on wavelet packet transform. IEEE Trans. Biomed. Eng. 57(7), 1639–1651 (2010)CrossRef
5.
go back to reference Hunyadi, B., et al.: Incorporating structural information from the multichannel EEG improves patient-specific seizure detection. Clin. Neurophysiol. 123(12), 2352–2361 (2012)CrossRef Hunyadi, B., et al.: Incorporating structural information from the multichannel EEG improves patient-specific seizure detection. Clin. Neurophysiol. 123(12), 2352–2361 (2012)CrossRef
6.
go back to reference Shoeb, A.H., Guttag, J.V.: Application of machine learning to epileptic seizure detection. In: International Conference on Machine Learning, pp. 975–982 (2010) Shoeb, A.H., Guttag, J.V.: Application of machine learning to epileptic seizure detection. In: International Conference on Machine Learning, pp. 975–982 (2010)
7.
go back to reference Baldassano, S., et al.: A novel seizure detection algorithm informed by hidden Markov model event states. J. Neural Eng. 13(3), 036011 (2016)CrossRef Baldassano, S., et al.: A novel seizure detection algorithm informed by hidden Markov model event states. J. Neural Eng. 13(3), 036011 (2016)CrossRef
8.
go back to reference Brand, M., Oliver, N., Pentland, A.: Coupled hidden Markov models for complex action recognition. In: Proceedings of the 1997 IEEE Computer Society Conference on Computer vision and pattern recognition, pp. 994–999. IEEE (1997) Brand, M., Oliver, N., Pentland, A.: Coupled hidden Markov models for complex action recognition. In: Proceedings of the 1997 IEEE Computer Society Conference on Computer vision and pattern recognition, pp. 994–999. IEEE (1997)
9.
go back to reference 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)CrossRef 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)CrossRef
10.
go back to reference Murphy, K.P.: Machine Learning: A Probabilistic Perspective. The MIT Press, Cambridge (2012)MATH Murphy, K.P.: Machine Learning: A Probabilistic Perspective. The MIT Press, Cambridge (2012)MATH
Metadata
Title
A Novel Method for Epileptic Seizure Detection Using Coupled Hidden Markov Models
Authors
Jeff Craley
Emily Johnson
Archana Venkataraman
Copyright Year
2018
DOI
https://doi.org/10.1007/978-3-030-00931-1_55

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