Higher-order tensor decomposition based scalp-to-intracranial EEG projection for detection of interictal epileptiform discharges. (24th December 2021)

• Record Type: Journal Article
• Authors: Abdi-Sargezeh, Bahman; Valentin, Antonio; Alarcon, Gonzalo; Martin-Lopez, David; Sanei, Saeid
• Is Part Of: Journal of neural engineering. Volume 18:Number 6(2021)
• Publication Date: 2021-12-24
• Subjects: IED detection -- interictal epileptiform discharge -- scalp to intracranial EEG mapping -- tensor decomposition; Neurosciences -- Periodicals; Biomedical engineering -- Periodicals; 612.8
• DOI: 10.1088/1741-2552/ac3cc4
• Languages: English
• ISSNs: 1741-2560

Abstract

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.