Prediction of central neuropathic pain in spinal cord injury based on EEG classifier

Vuckovic, A., Jose Ferrer Gallardo, V., Jarjees, M., Fraser, M. and Purcell, M. (2018) Prediction of central neuropathic pain in spinal cord injury based on EEG classifier. Clinical Neurophysiology, 129, pp. 1605-1617. (doi: 10.1016/j.clinph.2018.04.750) (PMID:29886266)

[img]
Preview
Text
163188.pdf - Accepted Version
Available under License Creative Commons Attribution Non-commercial No Derivatives.

1MB

Abstract

Objectives: To create a classifier based on electroencephalography (EEG) to identify spinal cord injured (SCI) participants at risk of developing central neuropathic pain (CNP) by comparing them with patients who had already developed pain and with able bodied controls. Methods: Multichannel EEG was recorded in the relaxed eyes opened and eyes closed states in 10 able bodied participants and 31 subacute SCI participants (11 with CNP, 10 without NP and 10 who later developed pain within 6 months of the EEG recording). Up to nine EEG band power features were classified using linear and non-linear classifiers. Results: Three classifiers (artificial neural networks ANN, support vector machine SVM and linear discriminant analysis LDA) achieved similar average performances, higher than 85% on a full set of features identifying patients at risk of developing pain and achieved comparably high performance classifying between other groups. With only 10 channels, LDA and ANN achieved 86% and 83% accuracy respectively, identifying patients at risk of developing CNP. Conclusion: Transferable learning classifier can detect patients at risk of developing CNP. EEG markers of pain appear before its physical symptoms. Simple and complex classifiers have comparable performance. Significance: Identify patients to receive prophylaxic treatment of CNP.

Item Type:Articles
Additional Information:This work was partially supported by the Higher Committee for Education Development, Iraq.
Status:Published
Refereed:Yes
Glasgow Author(s) Enlighten ID:Purcell, Mariel and Jarjees, Mr Mohammed Sabah and Vuckovic, Dr Aleksandra
Authors: Vuckovic, A., Jose Ferrer Gallardo, V., Jarjees, M., Fraser, M., and Purcell, M.
College/School:College of Medical Veterinary and Life Sciences > School of Medicine, Dentistry & Nursing
College of Science and Engineering > School of Engineering
College of Science and Engineering > School of Engineering > Biomedical Engineering
Journal Name:Clinical Neurophysiology
Publisher:Elsevier
ISSN:1388-2457
ISSN (Online):1872-8952
Published Online:23 May 2018
Copyright Holders:Copyright © 2018 International Federation of Clinical Neurophysiology
First Published:First published in Clinical Neurophysiology 129:1605-1617
Publisher Policy:Reproduced in accordance with the publisher copyright policy

University Staff: Request a correction | Enlighten Editors: Update this record