SZLoc: A Multi-resolution Architecture for Automated Epileptic Seizure Localization from Scalp EEG

Jeff Craley, Emily Johnson, Christophe Jouny, David Hsu, Raheel Ahmed, Archana Venkataraman
Proceedings of The 5th International Conference on Medical Imaging with Deep Learning, PMLR 172:261-281, 2022.

Abstract

We propose an end-to-end deep learning framework for epileptic seizure localization from scalp electroencephalography (EEG). Our architecture, SZLoc, extracts multi-resolution information via local (single channel) and global (cross-channel) CNN encodings. These interconnected representations are fused using a transformer layer. Leveraging its multi-resolution outputs, SZLoc derives three clinically interpretable outputs: electrode-level seizure activity, seizure onset zone localization, and identification of the EEG signal intervals that contribute to the final localization. From an optimization standpoint, we formulate a novel ensemble of loss functions to train SZLoc using inexact spatial and temporal labels of seizure onset. In this manner, SZLoc automatically learns phenomena at finer resolutions than the training labels. We validate our SZLoc framework and training paradigm on a clinical EEG dataset of 34 focal epilepsy patients. As compared to other deep learning baseline models, SZLoc achieves robust inter-patient seizure localization performance. We also demonstrate generalization of SZLoc to a second cohort of 16 epilepsy patients with different seizure characteristics and recorded at a different site. Taken together, SZLoc extends beyond the traditional paradigm of seizure detection by providing clinically relevant seizure localization information from coarse and inexact training labels.

Cite this Paper

BibTeX
@InProceedings{pmlr-v172-craley22a, title = {SZLoc: A Multi-resolution Architecture for Automated Epileptic Seizure Localization from Scalp EEG}, author = {Craley, Jeff and Johnson, Emily and Jouny, Christophe and Hsu, David and Ahmed, Raheel and Venkataraman, Archana}, booktitle = {Proceedings of The 5th International Conference on Medical Imaging with Deep Learning}, pages = {261--281}, year = {2022}, editor = {Konukoglu, Ender and Menze, Bjoern and Venkataraman, Archana and Baumgartner, Christian and Dou, Qi and Albarqouni, Shadi}, volume = {172}, series = {Proceedings of Machine Learning Research}, month = {06--08 Jul}, publisher = {PMLR}, pdf = {https://proceedings.mlr.press/v172/craley22a/craley22a.pdf}, url = {https://proceedings.mlr.press/v172/craley22a.html}, abstract = {We propose an end-to-end deep learning framework for epileptic seizure localization from scalp electroencephalography (EEG). Our architecture, SZLoc, extracts multi-resolution information via local (single channel) and global (cross-channel) CNN encodings. These interconnected representations are fused using a transformer layer. Leveraging its multi-resolution outputs, SZLoc derives three clinically interpretable outputs: electrode-level seizure activity, seizure onset zone localization, and identification of the EEG signal intervals that contribute to the final localization. From an optimization standpoint, we formulate a novel ensemble of loss functions to train SZLoc using inexact spatial and temporal labels of seizure onset. In this manner, SZLoc automatically learns phenomena at finer resolutions than the training labels. We validate our SZLoc framework and training paradigm on a clinical EEG dataset of 34 focal epilepsy patients. As compared to other deep learning baseline models, SZLoc achieves robust inter-patient seizure localization performance. We also demonstrate generalization of SZLoc to a second cohort of 16 epilepsy patients with different seizure characteristics and recorded at a different site. Taken together, SZLoc extends beyond the traditional paradigm of seizure detection by providing clinically relevant seizure localization information from coarse and inexact training labels.} }
Endnote
%0 Conference Paper %T SZLoc: A Multi-resolution Architecture for Automated Epileptic Seizure Localization from Scalp EEG %A Jeff Craley %A Emily Johnson %A Christophe Jouny %A David Hsu %A Raheel Ahmed %A Archana Venkataraman %B Proceedings of The 5th International Conference on Medical Imaging with Deep Learning %C Proceedings of Machine Learning Research %D 2022 %E Ender Konukoglu %E Bjoern Menze %E Archana Venkataraman %E Christian Baumgartner %E Qi Dou %E Shadi Albarqouni %F pmlr-v172-craley22a %I PMLR %P 261--281 %U https://proceedings.mlr.press/v172/craley22a.html %V 172 %X We propose an end-to-end deep learning framework for epileptic seizure localization from scalp electroencephalography (EEG). Our architecture, SZLoc, extracts multi-resolution information via local (single channel) and global (cross-channel) CNN encodings. These interconnected representations are fused using a transformer layer. Leveraging its multi-resolution outputs, SZLoc derives three clinically interpretable outputs: electrode-level seizure activity, seizure onset zone localization, and identification of the EEG signal intervals that contribute to the final localization. From an optimization standpoint, we formulate a novel ensemble of loss functions to train SZLoc using inexact spatial and temporal labels of seizure onset. In this manner, SZLoc automatically learns phenomena at finer resolutions than the training labels. We validate our SZLoc framework and training paradigm on a clinical EEG dataset of 34 focal epilepsy patients. As compared to other deep learning baseline models, SZLoc achieves robust inter-patient seizure localization performance. We also demonstrate generalization of SZLoc to a second cohort of 16 epilepsy patients with different seizure characteristics and recorded at a different site. Taken together, SZLoc extends beyond the traditional paradigm of seizure detection by providing clinically relevant seizure localization information from coarse and inexact training labels.
APA
Craley, J., Johnson, E., Jouny, C., Hsu, D., Ahmed, R. & Venkataraman, A.. (2022). SZLoc: A Multi-resolution Architecture for Automated Epileptic Seizure Localization from Scalp EEG. Proceedings of The 5th International Conference on Medical Imaging with Deep Learning, in Proceedings of Machine Learning Research 172:261-281 Available from https://proceedings.mlr.press/v172/craley22a.html.

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