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Detection of Moderate Traumatic Brain Injury from Resting-State Eye-Closed Electroencephalography
المؤلفون المشاركون
Abdullah, Jafri Malin
Abdullah, M. Z.
Lai, Chi Qin
Azman, Azlinda
Abd. Hamid, Aini Ismafairus
Ibrahim, Haidi
المصدر
Computational Intelligence and Neuroscience
العدد
المجلد 2020، العدد 2020 (31 ديسمبر/كانون الأول 2020)، ص ص. 1-10، 10ص.
الناشر
Hindawi Publishing Corporation
تاريخ النشر
2020-03-11
دولة النشر
مصر
عدد الصفحات
10
التخصصات الرئيسية
الملخص 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.
نمط استشهاد جمعية علماء النفس الأمريكية (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
نمط استشهاد الجمعية الأمريكية للغات الحديثة (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
نمط استشهاد الجمعية الطبية الأمريكية (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
نوع البيانات
مقالات
لغة النص
الإنجليزية
الملاحظات
Includes bibliographical references
رقم السجل
BIM-1138969
قاعدة معامل التأثير والاستشهادات المرجعية العربي "ارسيف Arcif"
أضخم قاعدة بيانات عربية للاستشهادات المرجعية للمجلات العلمية المحكمة الصادرة في العالم العربي
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