Incorporating uncertainty in data labeling into automatic detection of interictal epileptiform discharges from concurrent scalp EEG via multi-way analysis

Abdi-Sargezeh, B., Valentin, A., Alarcon, G. and Sanei, S. ORCID: 0000-0002-3437-2801, 2021. Incorporating uncertainty in data labeling into automatic detection of interictal epileptiform discharges from concurrent scalp EEG via multi-way analysis. International Journal of Neural Systems. ISSN 0129-0657

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Abstract

Interictal epileptiform discharges (IEDs) are elicited from an epileptic brain, whereas they can also be due to other neurological abnormalities. The diversity in their morphologies, their strengths, and their sources within the brain cause a great deal of uncertainty in their labeling by clinicians. The aim of this study is therefore to exploit and incorporate this uncertainty (the probability of the waveform being an IED) in the IED detection system which combines spatial component analysis (SCA) with the IED probabilities referred to as SCA-IEDP-based method. For comparison, we also propose and study SCA-based method in which probability of the waveform being an IED is ignored. The proposed models are employed to detect IEDs in two different classification approaches: (1) subject-dependent and (2) subject-independent classification approaches. The proposed methods are compared with two other state-of-the-art methods namely, time-frequency features and tensor factorization methods. The proposed SCA-IEDP model has achieved superior performance in comparison with the traditional SCA and other competing methods. It achieved 79.9% and 63.4% accuracy values in subject-dependent and subject-independent classification approaches, respectively. This shows that considering the IED probabilities in designing an IED detection system can boost its performance.

Item Type: Journal article
Publication Title: International Journal of Neural Systems
Creators: Abdi-Sargezeh, B., Valentin, A., Alarcon, G. and Sanei, S.
Publisher: World Scientific Pub Co Pte Lt
Date: 16 February 2021
ISSN: 0129-0657
Identifiers:
NumberType
10.1142/s0129065721500192DOI
1424080Other
Divisions: Schools > School of Science and Technology
Record created by: Linda Sullivan
Date Added: 11 Mar 2021 10:14
Last Modified: 31 May 2021 15:05
URI: http://irep.ntu.ac.uk/id/eprint/42483

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