Sparse common feature analysis for detection of interictal epileptiform discharges from concurrent scalp EEG

Abdi-Sargezeh, B ORCID: https://orcid.org/0000-0003-1141-0702, Valentin, A, Alarcon, G and Sanei, S ORCID: https://orcid.org/0000-0002-3437-2801, 2022. Sparse common feature analysis for detection of interictal epileptiform discharges from concurrent scalp EEG. IEEE Access. ISSN 2169-3536

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Abstract

Temporal interictal epileptiform discharges (IEDs) are often invisible in the scalp EEG (sEEG). However, due to within-electrode temporal correlation and between-electrode spatial correlation, they still have their signatures in the sEEG. Therefore, it is expected to have some common spatial and temporal features among the IEDs. In this paper, we first present a novel method, called common feature analysis (CFA)-based method, for IED detection via an existing common orthogonal basis extraction (COBE) algorithm. In the second approach, we benefit from the sparsity of IED waveforms in developing a new algorithm, namely sparse COBE, and based on that, a sparse CFA (SCFA)-based method for IED detection. The proposed CFA and SCFA models are compared with two state-of-the-art IED detection methods. Two types of approaches, namely within- and between-subject classification approaches, are employed for evaluating the methods. SCFA outperforms the others and achieves the accuracy values of 75.1% and 67.8% using within- and between-subject classification approaches, respectively. This enables the proposed techniques to capture the intracranial biomarkers of epilepsy and ameliorate the performance of a classifier in automatically detecting the scalp-invisible IEDs from sEEG.

Item Type:	Journal article
Publication Title:	IEEE Access
Creators:	Abdi-Sargezeh, B., Valentin, A., Alarcon, G. and Sanei, S.
Publisher:	Institute of Electrical and Electronics Engineers (IEEE)
Date:	14 April 2022
ISSN:	2169-3536
Identifiers:	Number Type 10.1109/access.2022.3167433 DOI 1538557 Other
Rights:	This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/
Divisions:	Schools > School of Science and Technology
Record created by:	Laura Ward
Date Added:	20 Apr 2022 08:34
Last Modified:	20 Apr 2022 08:34
URI:	https://irep.ntu.ac.uk/id/eprint/46138

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