Research ArticleNo Access

Incorporating Uncertainty in Data Labeling into Automatic Detection of Interictal Epileptiform Discharges from Concurrent Scalp-EEG via Multi-way Analysis

Bahman Abdi-Sargezeh

School of Science and Technology, Nottingham Trent University, Nottingham, UK

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Antonio Valentin

Department of Clinical Neuroscience, King’s College London, London, UK

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Gonzalo Alarcon

Department of Neurology, Hamad General Hospital, Doha, Qatar

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, and

Saeid Sanei

School of Science and Technology, Nottingham Trent University, Nottingham, UK

E-mail Address: [email protected]

Corresponding author.

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https://doi.org/10.1142/S0129065721500192Cited by:11 (Source: Crossref)

This article is part of the issue:

Special Issue: Computational and Technological Innovations for Epilepsy Diagnosis and Control
Guest Editors: Vasilios K. Kimiskidis and Steven Schachter

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.

Keywords:

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