Detection of Moderate Traumatic Brain Injury from Resting-State Eye-Closed Electroencephalography
Joint Authors
Abdullah, Jafri Malin
Abdullah, M. Z.
Lai, Chi Qin
Azman, Azlinda
Abd. Hamid, Aini Ismafairus
Ibrahim, Haidi
Source
Computational Intelligence and Neuroscience
Issue
Vol. 2020, Issue 2020 (31 Dec. 2020), pp.1-10, 10 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2020-03-11
Country of Publication
Egypt
No. of Pages
10
Main Subjects
Abstract EN
Traumatic brain injury (TBI) is one of the injuries that can bring serious consequences if medical attention has been delayed.
Commonly, analysis of computed tomography (CT) or magnetic resonance imaging (MRI) is required to determine the severity of a moderate TBI patient.
However, due to the rising number of TBI patients these days, employing the CT scan or MRI scan to every potential patient is not only expensive, but also time consuming.
Therefore, in this paper, we investigate the possibility of using electroencephalography (EEG) with computational intelligence as an alternative approach to detect the severity of moderate TBI patients.
EEG procedure is much cheaper than CT or MRI.
Although EEG does not have high spatial resolutions as compared with CT and MRI, it has high temporal resolutions.
The analysis and prediction of moderate TBI from EEG using conventional computational intelligence approaches are tedious as they normally involve complex preprocessing, feature extraction, or feature selection of the signal.
Thus, we propose an approach that uses convolutional neural network (CNN) to automatically classify healthy subjects and moderate TBI patients.
The input to this computational intelligence system is the resting-state eye-closed EEG, without undergoing preprocessing and feature selection.
The EEG dataset used includes 15 healthy volunteers and 15 moderate TBI patients, which is acquired at the Hospital Universiti Sains Malaysia, Kelantan, Malaysia.
The performance of the proposed method has been compared with four other existing methods.
With the average classification accuracy of 72.46%, the proposed method outperforms the other four methods.
This result indicates that the proposed method has the potential to be used as a preliminary screening for moderate TBI, for selection of the patients for further diagnosis and treatment planning.
American Psychological Association (APA)
Lai, Chi Qin& Ibrahim, Haidi& Abd. Hamid, Aini Ismafairus& Abdullah, M. Z.& Azman, Azlinda& Abdullah, Jafri Malin. 2020. Detection of Moderate Traumatic Brain Injury from Resting-State Eye-Closed Electroencephalography. Computational Intelligence and Neuroscience،Vol. 2020, no. 2020, pp.1-10.
https://search.emarefa.net/detail/BIM-1138969
Modern Language Association (MLA)
Lai, Chi Qin…[et al.]. Detection of Moderate Traumatic Brain Injury from Resting-State Eye-Closed Electroencephalography. Computational Intelligence and Neuroscience No. 2020 (2020), pp.1-10.
https://search.emarefa.net/detail/BIM-1138969
American Medical Association (AMA)
Lai, Chi Qin& Ibrahim, Haidi& Abd. Hamid, Aini Ismafairus& Abdullah, M. Z.& Azman, Azlinda& Abdullah, Jafri Malin. Detection of Moderate Traumatic Brain Injury from Resting-State Eye-Closed Electroencephalography. Computational Intelligence and Neuroscience. 2020. Vol. 2020, no. 2020, pp.1-10.
https://search.emarefa.net/detail/BIM-1138969
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1138969