research-article

Screening of Moderate Traumatic Brain Injury from Power Feature of Resting State Electroencephalography using Support Vector Machine

Authors:

Mohd Zaid Abdullah,

Jafri Malin Abdullah,

Haidi IbrahimAuthors Info & Claims

EEET 2019: Proceedings of the 2019 2nd International Conference on Electronics and Electrical Engineering Technology

Pages 99 - 103

https://doi.org/10.1145/3362752.3362758

Published: 25 September 2019 Publication History

Abstract

Traumatic brain injury (TBI) needs to be identified faster, so that suitable treatment can be planned properly. Normally, the severity of TBI is evaluated through the study from computed tomography (CT) or magnetic resonance imaging (MRI). Unfortunately, the number of CT scanners and MRI scanners is limited. Therefore, it is impractical to directly do CT or MRI scan to all patients without screening. Thus, this research investigates a method for screening moderate TBI patient. Data from resting state 63-channels electroencephalography is used in this work. Power of the signal is extracted from alpha, beta, theta and gamma frequency bands. This work utilizes a support vector machine, which is one of machine learning approaches, to identify moderate TBI patients. From the experimental results, it is shown that the average power from alpha or theta band gives the best accuracy score, which is at 70.83%.

References

[1]

Moppett, I. K. 2007. Traumatic brain injury: Assessment, resuscitation and early management. Brit. J. Anaesth. 99, 1(Jul. 2007), 18--31. DOI= https://doi.org/10.1093/bja/aem128

[2]

Udekwu, P., Schiro, S., Vaslef, S., Baker, C., and Oller, D. 2004. Glasgow coma scale score, mortality, and functional outcome in head-injured patients. J. Trauma. 56, 5(May 2004), 1084--1089. DOI= 10.1097/01.ta.0000124283.02605.a5

[3]

Papa, L., Robinson, G., Oli, M., Pineda, J., Demery, J., Brophy, G., Robicsek, S. A., Gabrielli, A., Robertson, C. S., Wang, K. K., and Hayes, R. L. 2008. Use of biomarkers for diagnosis and management of traumatic brain injury patients. Expert Opinion on Medical Diagnostics. 2, 8(Aug. 2008), 937--945. DOI= https://doi.org/10.1517/17530059.2.8.937

[4]

Maas, A. I., Stocchetti, N., and Bullock, R. 2008. Moderate and severe traumatic brain injury in adults. Lancet Neurol. 7, 8(Aug. 2008), 728--741. DOI = 10.1016/S1474-4422(08)70164-9

[5]

Vitaz, T. W., Jenks, J., Raque, G. H., and Shields, C. B. 2003. Outcome following moderate traumatic brain injury. Surg. Neurol. 60, 4(Oct. 2003), 285--291.

[6]

Fabbri, A., Servadei, F., Marchesini, G., Stein, S. C., and Vandelli, A. 2008. Early predictors of unfavourable outcome in subjects with moderate head injury in the emergency department. J.Neurol. Neurosurg. Psychiatry. 79, 5(May 2008), 567--573. DOI= 10.1136/jnnp.2007.120162

[7]

Griffey, R. T., and Sodickson, A. 2009. Cumulative radiation exposure and cancer risk estimates in emergency department patients undergoing repeat or multiple CT. AJR Am. J. Roentgenol. 192, 4(Apr. 2009), 887--892. DOI= 10.2214/AJR.08.1351

[8]

Gevins, A., Leong, H., Smith, M. E., Le, J., and Du, R. 1995. Mapping cognitive brain function with modern high-resolution electroencephalography. Trends Neurosci. 18, 10 (Oct. 1995), 429--436.

[9]

Reeves, T. M., and Colley, B. S. 2012. Electrophysiological Approaches in Traumatic Brain Injury. Humana Press, Totowa, NJ.

[10]

Arciniegas, D. B. 2011. Clinical electrophysiologic assessments and mild traumatic brain injury: State-of-the-science and implications for clinical practice. Int. J. Psychophysiol. 82, 1(Oct. 2011), 41--52. DOI= 10.1016/j.ijpsycho.2011.03.004

[11]

Hanley, D., Prichep, L. S., Badjatia, N., Bazarian, J., Chiacchierini, R., Curley, K. C., Garrett, J., Jones, E., Naunheim, R., O'Neil, B., O'Neill, J., Wright, D. W., and Huff, J. S. 2018. A brain electrical activity electroencephalographic-based biomarker of functional impairment in traumatic brain injury: A multi-site validation trial. J. Neurotrauma. 35, 1(Jan. 2018), 41--47. DOI= 10.1089/neu.2017.5004

[12]

Ianof, J., and Anghinah, R. 2017. Traumatic brain injury: An EEG point of view. Dement. Neuropsychol. 11, 1(Jan.-Mar. 2017), 3--5. DOI= 10.1590/1980-57642016dn11-010002.

[13]

Tolonen, A., Srkel, M. O. K., Takala, R. S. K., Katila, A., Frantzn, J., Posti, J. P., Mller, M., van Gils, M., and Tenovuo, O. 2018. Quantitative EEG parameters for prediction of outcome in severe traumatic brain injury: Development study. Clin. EEG Neurosci. 49, 4(Jul. 2018), 248--257. DOI= https://doi.org/10.1177/1550059417742232

[14]

Fisher, J. A. N., Huang, S., Ye, M., Nabili, M., Wilent, W. B., Krauthamer, V., Myers, M. R., and Welle, C. G. 2016. Real-time detection and monitoring of acute brain injury utilizing evoked electroencephalographic potentials. IEEE Trans. Neural Syst. Rehabil. Eng. 24, 9(Sep. 2016), 1003--1012. DOI= 10.1109/TNSRE.2016.2529663

[15]

McBride, J., Zhao, X., Nichols, T., Vagnini, V., Munro, N., Berry, D., and Jiang, Y. 2013. Scalp EEG-based discrimination of cognitive deficits after traumatic brain injury using event-related Tsallis entropy analysis. IEEE Trans. Biomed. Eng. 60, 1(Jan. 2013), 90--96. DOI= 10.1109/TBME.2012.2223698

[16]

Yadav, N. K., and Ciuffreda, K. J. 2015. Objective assessment of visual attention in mild traumatic brain injury (mTBI) using visual-evoked potentials (vep). Brain Inj. 29, 3(Nov. 2015), 352--365. DOI= https://doi.org/10.3109/02699052.2014.979229

[17]

Schmitt, S., and Dichter, M. A. 2015. Chapter 21 - Electrophysiologic recordings in traumatic brain injury in Traumatic Brain Injury, Part I, ser. Handbook of Clinical Neurology, J. Grafman and A. M. Salazar, Eds. Elsevier, 2015, vol. 127, pp. 319--339.

[18]

Basar, E., Gnder, A., and Ungan, P. 1976. Important relation between EEG and brain evoked potentials. I. Resonance phenomena in subdural structures of the cat brain. Biol. Cyber. 25, 1(Dec. 1976), 27--40.

[19]

R. V. den Brink, S. Nieuwenhuis, G. van Boxtel, G. van Luijtelaar, H. Eilander, and V. Wijnen, "Task-free spectral EEG dynamics track and predict patient recovery from severe acquired brain injury," NeuroImage: Clinical, vol. 17, pp. 43--52, 2018. [Online]. Available: http://www.sciencedirect.com/science/article/pii/S2213158217302449

[20]

McNerney, M. W., Hobday, T., Cole, B., Ganong, R., Winans, N., Matthews, D., Hood, J., and Lane, S. 2019. Objective classification of mTBI using machine learning on a combination of frontopolar electroencephalography measurements and self-reported symptoms. Sports Med. Open. 5, 1(Apr. 2019), 14. DOI= https://doi.org/10.1186/s40798-019-0187-y

[21]

Lai, C. Q., Ibrahim, H., Abdullah, M.Z., Abdullah, J.M., Suandi, S. A., and Azman, A. 2019. Arrangement of resting state electroencephalography as the input to convolutional neural network for biometric identification. Comput. Intell. Neurosci. 2019, Article ID 7895924, 10 pages. DOI= https://doi.org/10.1155/2019/7895924

[22]

Hine, G. E., Maiorana, E., and Campisi, P. 2017. Resting-state EEG: A study on its non-stationarity for biometric applications. In 2017 International Conference of the Biometrics Special Interest Group (BIOSIG), (Darmstadt, Germany, September 20-22, 2017), IEEE, 1--5. DOI= 10.23919/BIOSIG.2017.8053519

[23]

Lai, C. Q., Ibrahim, H., Abdullah, M.Z., Abdullah, J.M., Suandi, S. A., and Azman, A. 2018. Artifacts and noise removal for electroencephalogram (EEG): A literature review. In 2018 IEEE Symposium on Computer Applications Industrial Electronics (ISCAIE), (Penang, Malaysia, 28-29 April 2018), IEEE, 326--332. DOI= 10.1109/ISCAIE.2018.8405493

[24]

Lai, C. Q., Ibrahim, H., Abdullah, M.Z., Abdullah, J.M., Suandi, S. A., and Azman, A. 2019. Features from electroencephalogram (EEG) for signal analysis. Journal of Advanced Research in Dynamical Control Systems, 11, 03-Special Issue, 1781--1787.

[25]

Hsu, C.-W., Chang, C.-C., and Lin, C.-J. 2003. A Practical Guide to Support Vector Classification. Technical Report. Department of Computer Science, National Taiwan

Cited By

Mohd Noor NIbrahim HChe Lah MAbdullah J(2022)Prediction of Recovery from Traumatic Brain Injury with EEG Power Spectrum in Combination of Independent Component Analysis and RUSBoost ModelBioMedInformatics10.3390/biomedinformatics20100072:1(106-123)Online publication date: 6-Jan-2022
https://doi.org/10.3390/biomedinformatics2010007
Alouani AElfouly T(2022)Traumatic Brain Injury (TBI) Detection: Past, Present, and FutureBiomedicines10.3390/biomedicines1010247210:10(2472)Online publication date: 3-Oct-2022
https://doi.org/10.3390/biomedicines10102472
Lai CAzman AAbdullah JIbrahim H(2022)Convolutional Neural Network with Hidden Markov Model to Identify Non-severe Traumatic Brain Injury from ElectroencephalographyProceedings of the 11th International Conference on Robotics, Vision, Signal Processing and Power Applications10.1007/978-981-16-8129-5_70(455-460)Online publication date: 1-Jan-2022
https://doi.org/10.1007/978-981-16-8129-5_70
Show More Cited By

Index Terms

Screening of Moderate Traumatic Brain Injury from Power Feature of Resting State Electroencephalography using Support Vector Machine

Recommendations

Detection of Moderate Traumatic Brain Injury from Resting-State Eye-Closed Electroencephalography

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 ...
Multi-parametric classification of traumatic brain injury patients using automatic analysis of quantitative MRI scans
MIAR'10: Proceedings of the 5th international conference on Medical imaging and augmented reality

Traumatic brain injury (TBI) is ranked as the fourth highest cause of death in the developed world. The majority of patients sustain mild TBI, and a significant number suffer persistent neuropsychological problems. Conventional neuroimaging methods (CT, ...
Automatic EEG Processing for the Early Diagnosis of Traumatic Brain Injury

Traumatic Brain Injury (TBI) is recognized as an important cause of death and disabilities after an accident. The availability a tool for the early diagnosis of brain dysfunctions could greatly improve the quality of life of people affected by TBI and ...

Comments

Information & Contributors

Information

Published In

cover image ACM Other conferences

EEET 2019: Proceedings of the 2019 2nd International Conference on Electronics and Electrical Engineering Technology

September 2019

160 pages

ISBN:9781450372145

DOI:10.1145/3362752

Copyright © 2019 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

In-Cooperation

USM: Universiti Sains Malaysia

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 25 September 2019

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article
Research
Refereed limited

Funding Sources

Ministry of Higher Education, Malaysia

Conference

EEET 2019

EEET 2019: 2019 2nd International Conference on Electronics and Electrical Engineering Technology

September 25 - 27, 2019

Penang, Malaysia

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

7
Total Citations
View Citations
81
Total Downloads

Downloads (Last 12 months)4
Downloads (Last 6 weeks)0

Reflects downloads up to 03 Mar 2025

Other Metrics

View Author Metrics

Citations

Cited By

Mohd Noor NIbrahim HChe Lah MAbdullah J(2022)Prediction of Recovery from Traumatic Brain Injury with EEG Power Spectrum in Combination of Independent Component Analysis and RUSBoost ModelBioMedInformatics10.3390/biomedinformatics20100072:1(106-123)Online publication date: 6-Jan-2022
https://doi.org/10.3390/biomedinformatics2010007
Alouani AElfouly T(2022)Traumatic Brain Injury (TBI) Detection: Past, Present, and FutureBiomedicines10.3390/biomedicines1010247210:10(2472)Online publication date: 3-Oct-2022
https://doi.org/10.3390/biomedicines10102472
Lai CAzman AAbdullah JIbrahim H(2022)Convolutional Neural Network with Hidden Markov Model to Identify Non-severe Traumatic Brain Injury from ElectroencephalographyProceedings of the 11th International Conference on Robotics, Vision, Signal Processing and Power Applications10.1007/978-981-16-8129-5_70(455-460)Online publication date: 1-Jan-2022
https://doi.org/10.1007/978-981-16-8129-5_70
Fiani BPasko KSarhadi KCovarrubias C(2021)Current uses, emerging applications, and clinical integration of artificial intelligence in neuroradiologyReviews in the Neurosciences10.1515/revneuro-2021-010133:4(383-395)Online publication date: 10-Sep-2021
https://doi.org/10.1515/revneuro-2021-0101
Lai CIbrahim HAbdullah JAzman AAbdullah M(2021)Convolutional Neural Network Utilizing Error-Correcting Output Codes Support Vector Machine for Classification of Non-Severe Traumatic Brain Injury From Electroencephalogram SignalIEEE Access10.1109/ACCESS.2021.30567249(24946-24964)Online publication date: 2021
https://doi.org/10.1109/ACCESS.2021.3056724
Lai CIbrahim HAbd Hamid AAbdullah J(2020)Classification of Non-Severe Traumatic Brain Injury from Resting-State EEG Signal Using LSTM Network with ECOC-SVMSensors10.3390/s2018523420:18(5234)Online publication date: 14-Sep-2020
https://doi.org/10.3390/s20185234
Lai CIbrahim HAbd. Hamid AAbdullah MAzman AAbdullah J(2020)Detection of Moderate Traumatic Brain Injury from Resting-State Eye-Closed ElectroencephalographyComputational Intelligence and Neuroscience10.1155/2020/89239062020Online publication date: 1-Jan-2020
https://dl.acm.org/doi/10.1155/2020/8923906

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Figures

Tables

Media

View Table of Conten