Higher-order tensor decomposition based scalp-to-intracranial EEG projection for detection of interictal epileptiform discharges

Abdi Sargezeh, B., Valentin, A., Alarcon, G., Martin-Lopez, D. and Sanei, S. ORCID: 0000-0002-3437-2801, 2021. Higher-order tensor decomposition based scalp-to-intracranial EEG projection for detection of interictal epileptiform discharges. Journal of Neural Engineering. ISSN 1741-2560

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Abstract

Objective. Interictal epileptiform discharges (IEDs) occur between two seizures onsets. IEDs are mainly captured by intracranial recordings and are often invisible over the scalp. This study proposes a model based on tensor factorization to map the time-frequency (TF) features of scalp EEG (sEEG) to the TF features of intracranial EEG (iEEG) in order to detect IEDs from over the scalp with high sensitivity.

Approach. Continuous wavelet transform is employed to extract the TF features. Time, frequency, and channel modes of IED segments from iEEG recordings are concatenated into a four-way tensor. Tucker and CANDECOMP/PARAFAC decomposition techniques are employed to decompose the tensor into temporal, spectral, spatial, and segmental factors. Finally, TF features of both IED and non-IED segments from scalp recordings are projected onto the temporal components for classification.

Main results. The model performance is obtained in two different approaches: within- and between-subject classification approaches. Our proposed method is compared with four other methods, namely a tensor-based spatial component analysis method, TF-based method, linear regression mapping model, and asymmetric-symmetric autoencoder mapping model followed by convolutional neural networks. Our proposed method outperforms all these methods in both within- and between-subject classification approaches by respectively achieving 84.2% and 72.6% accuracy values.

Significance. The findings show that mapping sEEG to iEEG improves the performance of the scalp-based IED detection model. Furthermore, the tensor-based mapping model outperforms the autoencoder- and regression-based mapping models.

Item Type: Journal article
Publication Title: Journal of Neural Engineering
Creators: Abdi Sargezeh, B., Valentin, A., Alarcon, G., Martin-Lopez, D. and Sanei, S.
Publisher: IOP Publishing
Date: 24 November 2021
ISSN: 1741-2560
Identifiers:
NumberType
10.1088/1741-2552/ac3cc4DOI
1500531Other
Rights: © 2021 The Author(s). Published by IOP Publishing Ltd. As the Version of Record of this article is going to be/has been published on a gold open access basis under a CC BY 3.0 licence, this Accepted Manuscript is available for reuse under a CC BY 3.0 licence immediately. Everyone is permitted to use all or part of the original content in this article, provided that they adhere to all the terms of the licence https://creativecommons.org/licences/by/3.0 Although reasonable endeavours have been taken to obtain all necessary permissions from third parties to include their copyrighted content within this article, their full citation and copyright line may not be present in this Accepted Manuscript version. Before using any content from this article, please refer to the Version of Record on IOPscience once published for full citation and copyright details, as permissions may be required. All third party content is fully copyright protected and is not published on a gold open access basis under a CC BY licence, unless that is specifically stated in the figure caption in the Version of Record.
Divisions: Schools > School of Science and Technology
Record created by: Laura Ward
Date Added: 02 Dec 2021 11:30
Last Modified: 02 Dec 2021 11:30
URI: http://irep.ntu.ac.uk/id/eprint/45033

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