Seizure recognition on epilepsy feature tensor

Evrim Acar; Canan Aykut Bingos; Haluk Bingol; Rasmus Bro; Bülent Yener

doi:10.1109/IEMBS.2007.4353280

Seizure recognition on epilepsy feature tensor

Evrim Acar^*, Canan Aykut Bingos, Haluk Bingol, Rasmus Bro, Bülent Yener

^*Corresponding author af dette arbejde

Food Analytics and Biotechnology

20 Citationer (Scopus)

Abstract

With a goal of automating visual analysis of electroencephalogram (EEG) data and assessing the performance of various features in seizure recognition, we introduce a mathematical model capable of recognizing patient-specific epileptic seizures with high accuracy. We represent multi-channel scalp EEG using a set of features. These features expected to have distinct trends during seizure and non-seizure periods include features from both time and frequency domains. The contributions of this paper are threefold. First, we rearrange multi-channel EEG signals as a third-order tensor called an Epilepsy Feature Tensor with modes: time epochs, features and electrodes. Second, we model the Epilepsy Feature Tensor using a multilinear regression model, i.e., Multilinear Partial Least Squares regression, which is the generalization of Partial Least Squares (PLS) regression to higher-order datasets. This two-step approach facilitates EEG data analysis from multiple electrodes represented by several features from different domains. Third, we identify which features are more significant for seizure recognition. Our results based on the analysis of 19 seizures from 5 epileptic patients demonstrate that multiway analysis of an Epilepsy Feature Tensor can detect (patient-specific) seizures with classification accuracy ranging between 77-96%.

Originalsprog	Engelsk
Titel	29th Annual International Conference of IEEE-EMBS, Engineering in Medicine and Biology Society, EMBC'07
Antal sider	4
Publikationsdato	1 dec. 2007
Sider	4273-4276
Artikelnummer	4353280
ISBN (Trykt)	1424407885, 9781424407880
DOI	https://doi.org/10.1109/IEMBS.2007.4353280
Status	Udgivet - 1 dec. 2007
Begivenhed	29th Annual International Conference of IEEE-EMBS, Engineering in Medicine and Biology Society, EMBC'07 - Lyon, Frankrig Varighed: 23 aug. 2007 → 26 aug. 2007

Konference

Konference	29th Annual International Conference of IEEE-EMBS, Engineering in Medicine and Biology Society, EMBC'07
Land/Område	Frankrig
By	Lyon
Periode	23/08/2007 → 26/08/2007
Sponsor	ACIES, Research Promotion and Management Consulting, EOARD, European Office of Aerospace R and D, Grand Lyon, NSF, National Science Foundation, Philips Research Europe, Philips Research North America

Navn	Annual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings
ISSN	0589-1019

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10.1109/IEMBS.2007.4353280

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Citationsformater

Acar, E., Bingos, C. A., Bingol, H., Bro, R., & Yener, B. (2007). Seizure recognition on epilepsy feature tensor. I 29th Annual International Conference of IEEE-EMBS, Engineering in Medicine and Biology Society, EMBC'07 (s. 4273-4276). [4353280] Annual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings https://doi.org/10.1109/IEMBS.2007.4353280

Seizure recognition on epilepsy feature tensor. / Acar, Evrim; Bingos, Canan Aykut; Bingol, Haluk et al.

29th Annual International Conference of IEEE-EMBS, Engineering in Medicine and Biology Society, EMBC'07. 2007. s. 4273-4276 4353280 (Annual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings).

Publikation: Bidrag til bog/antologi/rapport › Konferencebidrag i proceedings › Forskning › peer review

Acar, E, Bingos, CA, Bingol, H, Bro, R & Yener, B 2007, Seizure recognition on epilepsy feature tensor. i 29th Annual International Conference of IEEE-EMBS, Engineering in Medicine and Biology Society, EMBC'07., 4353280, Annual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings, s. 4273-4276, 29th Annual International Conference of IEEE-EMBS, Engineering in Medicine and Biology Society, EMBC'07, Lyon, Frankrig, 23/08/2007. https://doi.org/10.1109/IEMBS.2007.4353280

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title = "Seizure recognition on epilepsy feature tensor",

abstract = "With a goal of automating visual analysis of electroencephalogram (EEG) data and assessing the performance of various features in seizure recognition, we introduce a mathematical model capable of recognizing patient-specific epileptic seizures with high accuracy. We represent multi-channel scalp EEG using a set of features. These features expected to have distinct trends during seizure and non-seizure periods include features from both time and frequency domains. The contributions of this paper are threefold. First, we rearrange multi-channel EEG signals as a third-order tensor called an Epilepsy Feature Tensor with modes: time epochs, features and electrodes. Second, we model the Epilepsy Feature Tensor using a multilinear regression model, i.e., Multilinear Partial Least Squares regression, which is the generalization of Partial Least Squares (PLS) regression to higher-order datasets. This two-step approach facilitates EEG data analysis from multiple electrodes represented by several features from different domains. Third, we identify which features are more significant for seizure recognition. Our results based on the analysis of 19 seizures from 5 epileptic patients demonstrate that multiway analysis of an Epilepsy Feature Tensor can detect (patient-specific) seizures with classification accuracy ranging between 77-96%.",

author = "Evrim Acar and Bingos, {Canan Aykut} and Haluk Bingol and Rasmus Bro and B{\"u}lent Yener",

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AU - Bingos, Canan Aykut

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AU - Yener, Bülent

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N2 - With a goal of automating visual analysis of electroencephalogram (EEG) data and assessing the performance of various features in seizure recognition, we introduce a mathematical model capable of recognizing patient-specific epileptic seizures with high accuracy. We represent multi-channel scalp EEG using a set of features. These features expected to have distinct trends during seizure and non-seizure periods include features from both time and frequency domains. The contributions of this paper are threefold. First, we rearrange multi-channel EEG signals as a third-order tensor called an Epilepsy Feature Tensor with modes: time epochs, features and electrodes. Second, we model the Epilepsy Feature Tensor using a multilinear regression model, i.e., Multilinear Partial Least Squares regression, which is the generalization of Partial Least Squares (PLS) regression to higher-order datasets. This two-step approach facilitates EEG data analysis from multiple electrodes represented by several features from different domains. Third, we identify which features are more significant for seizure recognition. Our results based on the analysis of 19 seizures from 5 epileptic patients demonstrate that multiway analysis of an Epilepsy Feature Tensor can detect (patient-specific) seizures with classification accuracy ranging between 77-96%.

AB - With a goal of automating visual analysis of electroencephalogram (EEG) data and assessing the performance of various features in seizure recognition, we introduce a mathematical model capable of recognizing patient-specific epileptic seizures with high accuracy. We represent multi-channel scalp EEG using a set of features. These features expected to have distinct trends during seizure and non-seizure periods include features from both time and frequency domains. The contributions of this paper are threefold. First, we rearrange multi-channel EEG signals as a third-order tensor called an Epilepsy Feature Tensor with modes: time epochs, features and electrodes. Second, we model the Epilepsy Feature Tensor using a multilinear regression model, i.e., Multilinear Partial Least Squares regression, which is the generalization of Partial Least Squares (PLS) regression to higher-order datasets. This two-step approach facilitates EEG data analysis from multiple electrodes represented by several features from different domains. Third, we identify which features are more significant for seizure recognition. Our results based on the analysis of 19 seizures from 5 epileptic patients demonstrate that multiway analysis of an Epilepsy Feature Tensor can detect (patient-specific) seizures with classification accuracy ranging between 77-96%.

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Seizure recognition on epilepsy feature tensor

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