Multi-task multiple kernel learning reveals relevant frequency bands for critical areas localization in focal epilepsy

Vanessa D’Amario, Federico Tomasi, Veronica Tozzo, Gabriele Arnulfo, Annalisa Barla, Lino Nobili
Proceedings of the 3rd Machine Learning for Healthcare Conference, PMLR 85:348-382, 2018.

Abstract

The localization of epileptic zone in pharmacoresistant focal epileptic patients is a daunting task, typically performed by medical experts through visual inspection over highly sampled neural recordings. For a finer localization of the epileptogenic areas and a deeper understanding of the pathology both the identification of pathogenical biomarkers and the automatic characterization of epileptic signals are desirable. In this work we present a data integration learning method based on multi-level representation of stereo-electroencephalography recordings and multiple kernel learning. To the best of our knowledge, this is the first attempt to tackle both aspects simultaneously, as our approach is devised to classify critical vs. non-critical recordings while detecting the most discriminative frequency bands. The learning pipeline is applied to a data set of 18 patients for a total of 2347 neural recordings analyzed by medical experts. Without any prior knowledge assumption, the data-driven method reveals the most discriminative frequency bands for the localization of epileptic areas in the high-frequency spectrum (>=80 Hz) while showing high performance metric scores (mean balanced accuracy of 0.89 +- 0.03). The promising results may represent a starting point for the automatic search of clinical biomarkers of epileptogenicity.

Cite this Paper

BibTeX
@InProceedings{pmlr-v85-d-amario18a, title = {Multi-task multiple kernel learning reveals relevant frequency bands for critical areas localization in focal epilepsy}, author = {D'Amario, Vanessa and Tomasi, Federico and Tozzo, Veronica and Arnulfo, Gabriele and Barla, Annalisa and Nobili, Lino}, booktitle = {Proceedings of the 3rd Machine Learning for Healthcare Conference}, pages = {348--382}, year = {2018}, editor = {Doshi-Velez, Finale and Fackler, Jim and Jung, Ken and Kale, David and Ranganath, Rajesh and Wallace, Byron and Wiens, Jenna}, volume = {85}, series = {Proceedings of Machine Learning Research}, month = {17--18 Aug}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v85/d-amario18a/d-amario18a.pdf}, url = {https://proceedings.mlr.press/v85/d-amario18a.html}, abstract = {The localization of epileptic zone in pharmacoresistant focal epileptic patients is a daunting task, typically performed by medical experts through visual inspection over highly sampled neural recordings. For a finer localization of the epileptogenic areas and a deeper understanding of the pathology both the identification of pathogenical biomarkers and the automatic characterization of epileptic signals are desirable. In this work we present a data integration learning method based on multi-level representation of stereo-electroencephalography recordings and multiple kernel learning. To the best of our knowledge, this is the first attempt to tackle both aspects simultaneously, as our approach is devised to classify critical vs. non-critical recordings while detecting the most discriminative frequency bands. The learning pipeline is applied to a data set of 18 patients for a total of 2347 neural recordings analyzed by medical experts. Without any prior knowledge assumption, the data-driven method reveals the most discriminative frequency bands for the localization of epileptic areas in the high-frequency spectrum (>=80 Hz) while showing high performance metric scores (mean balanced accuracy of 0.89 +- 0.03). The promising results may represent a starting point for the automatic search of clinical biomarkers of epileptogenicity.} }
Endnote
%0 Conference Paper %T Multi-task multiple kernel learning reveals relevant frequency bands for critical areas localization in focal epilepsy %A Vanessa D’Amario %A Federico Tomasi %A Veronica Tozzo %A Gabriele Arnulfo %A Annalisa Barla %A Lino Nobili %B Proceedings of the 3rd Machine Learning for Healthcare Conference %C Proceedings of Machine Learning Research %D 2018 %E Finale Doshi-Velez %E Jim Fackler %E Ken Jung %E David Kale %E Rajesh Ranganath %E Byron Wallace %E Jenna Wiens %F pmlr-v85-d-amario18a %I PMLR %P 348--382 %U https://proceedings.mlr.press/v85/d-amario18a.html %V 85 %X The localization of epileptic zone in pharmacoresistant focal epileptic patients is a daunting task, typically performed by medical experts through visual inspection over highly sampled neural recordings. For a finer localization of the epileptogenic areas and a deeper understanding of the pathology both the identification of pathogenical biomarkers and the automatic characterization of epileptic signals are desirable. In this work we present a data integration learning method based on multi-level representation of stereo-electroencephalography recordings and multiple kernel learning. To the best of our knowledge, this is the first attempt to tackle both aspects simultaneously, as our approach is devised to classify critical vs. non-critical recordings while detecting the most discriminative frequency bands. The learning pipeline is applied to a data set of 18 patients for a total of 2347 neural recordings analyzed by medical experts. Without any prior knowledge assumption, the data-driven method reveals the most discriminative frequency bands for the localization of epileptic areas in the high-frequency spectrum (>=80 Hz) while showing high performance metric scores (mean balanced accuracy of 0.89 +- 0.03). The promising results may represent a starting point for the automatic search of clinical biomarkers of epileptogenicity.
APA
D’Amario, V., Tomasi, F., Tozzo, V., Arnulfo, G., Barla, A. & Nobili, L.. (2018). Multi-task multiple kernel learning reveals relevant frequency bands for critical areas localization in focal epilepsy. Proceedings of the 3rd Machine Learning for Healthcare Conference, in Proceedings of Machine Learning Research 85:348-382 Available from https://proceedings.mlr.press/v85/d-amario18a.html.

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