TY - JOUR
T1 - Incorporating Uncertainty in Data Labeling into Automatic Detection of Interictal Epileptiform Discharges from Concurrent Scalp-EEG via Multi-way Analysis
AU - Abdi-Sargezeh, Bahman
AU - Valentin, Antonio
AU - Alarcon, Gonzalo
AU - Sanei, Saeid
N1 - Publisher Copyright:
© 2021 World Scientific Publishing Company.
Copyright:
Copyright 2021 Elsevier B.V., All rights reserved.
PY - 2021/8
Y1 - 2021/8
N2 - 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.
AB - 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.
KW - EEG interictal epileptiform discharges
KW - IED detection
KW - IED morphology
KW - labeling uncertainty
KW - tensor decomposition
UR - http://www.scopus.com/inward/record.url?scp=85103519656&partnerID=8YFLogxK
U2 - 10.1142/S0129065721500192
DO - 10.1142/S0129065721500192
M3 - Article
AN - SCOPUS:85103519656
SN - 0129-0657
VL - 31
JO - International Journal of Neural Systems
JF - International Journal of Neural Systems
IS - 8
M1 - 2150019
ER -