Frontal Medial Theta-Based Intelligence System for Verifying Mild Traumatic Brain Injury
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Abstract
Purpose: The purpose of this study was to create an Intelligence System (IS) to analyze the Electroencephalogram (EEG) characteristics of patients with mild Traumatic Brain Injury (mTBI) and healthy volunteers. Generally, mTBI research demonstrates that patients suffer from Working Memory (WM). The frontal cortex is involved in the clinical physiology of mTBI and is crucial for delayed memory.
Materials and Methods: The Frontal-Medial Theta (FMT) is one of the most critical factors in mTBI verification. The oscillatory strength of FMT (4-8Hz) over the Frontal-Medial Cortex (FMC) or Supplementary Motor Area (SMA) and the medial-Sensory Motor Cortex (mSMC) is associated with efficient WM performance. The designed IS accesses the FMT of mTBI and healthy subjects by FCz and Cz electrodes placed in FMC or SMA and mSMC, respectively. The Multi-level Discrete Wavelet Transformation (MDWT) of EEG (FCz and Cz) is suggested here to investigate the mTBI. The FMT rhythms of EEG of FCz and Cz channels are extracted through 3-level-DWT. Then, 1768 features [712 features of healthy subjects + 1056 features of mTBI patients] for both the FCz and Cz electrodes were calculated via their FMT using eight statistical feature computations.
Results: The study found that the FMT strength of FCz and Cz electrodes is similar. The Bagging Classifier achieved 83.3333% accuracy with the 20-fold validation for the FCz electrode.
Conclusion: The strength of the FMT-FCz and FMT-Cz electrodes is approximately the same, and both are equally crucial to investigating mild Traumatic Brain Injury.
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28- P. Kaushik, A. Gupta, P. P. Roy, and D. P. Dogra, "EEG-Based Age and Gender Prediction Using Deep BLSTM-LSTM Network Model," IEEE Sens. J., vol. 19, no. 7, pp. 2634–2641, (2019), doi: 10.1109/JSEN.2018.2885582.
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30- G. Zhang, V. Davoodnia, A. Sepas-Moghaddam, Y. Zhang, and A. Etemad, "Classification of Hand Movements from EEG using a Deep Attention-based LSTM Network," (2019), doi: 10.1109/JSEN.2019.2956998.
2- S. A. Goldberg, D. Rojanasarntikul, and A. Jagoda, The prehospital management of traumatic brain injury, 1st ed., vol. 127. Elsevier B.V., (2015). doi: 10.1016/B978-0-444-52892-6.00023-4.
3- P. Udekwu, S. Kromhout-Schiro, S. Vaslef, C. Baker, and D. Oller, "Glasgow Coma Scale score, mortality, and functional outcome in head-injured patients," J. Trauma - Inj. Infect. Crit. Care, vol. 56, no. 5, pp. 1084–1089, (2004), doi: 10.1097/01.TA.0000124283.02605.A5.
4- K. Salottolo, A. Stewart Levy, D. S. Slone, C. W. Mains, and D. Bar-Or, "The effect of age on glasgow coma scale score in patients with traumatic brain injury," JAMA Surg., vol. 149, no. 7, pp. 727–734, (2014), doi: 10.1001/jamasurg.2014.13.
5- N. S. Dhillon, A. Sutandi, M. Vishwanath, M. M. Lim, H. Cao, and D. Si, "A raspberry pi-based traumatic brain injury detection system for single-channel electroencephalogram," Sensors, vol. 21, no. 8, (2021), doi: 10.3390/s21082779.
6- J. D. Lewine et al., "Quantitative EEG Biomarkers for Mild Traumatic Brain Injury," J. Clin. Neurophysiol., vol. 36, no. 4, pp. 298–305, (2019), doi: 10.1097/WNP.0000000000000588.
7- M. C. Ouellet, S. Beaulieu-Bonneau, and C. M. Morin, "Sleep-wake disturbances after traumatic brain injury," Lancet Neurol., vol. 14, no. 7, pp. 746–757, (2015), doi: 10.1016/S1474-4422(15)00068-X.
8- R. T. Griffey and A. Sodickson, "Cumulative radiation exposure and cancer risk estimates in emergency department patients undergoing repeat or multiple C.T.," Am. J. Roentgenol., vol. 192, no. 4, pp. 887–892, (2009), doi: 10.2214/AJR.08.1351.
9- C. Q. Lai, H. Ibrahim, A. I. Abd Hamid, M. Z. Abdullah, A. Azman, and J. M. Abdullah, "Detection of Moderate Traumatic Brain Injury from Resting-State Eye-Closed Electroencephalography," Comput. Intell. Neurosci., vol. 2020, (2020), doi: 10.1155/2020/8923906.
10- P. L. Nunez, "Toward a quantitive description of large-scale neocortical dynamic function and EEG," Behav. Brain Sci., vol. 23, no. 3, pp. 371–437, (2000), doi: 10.1017/S0140525X00003253.
11- R. P. Lystad and H. Pollard, "Functional neuroimaging: a brief overview and feasibility for use in chiropractic research.," J. Can. Chiropr. Assoc., vol. 53, no. 1, pp. 59–72, (2009), [Online]. Available: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2652631/.
12- T. Harmony, E. Marosi, A. E. Díaz de León, J. Becker, and T. Fernández, "Effect of sex, psychosocial disadvantages and biological risk factors on EEG maturation," Electroencephalogr. Clin. Neurophysiol., vol. 75, no. 6, pp. 482–491, (1990), doi: 10.1016/0013-4694(90)90135-7.
13- E. Zion-golumbic, "What is EEG ? Lecture notes for psychology," The Department of Psychology and the Department of Cognitive Science, Hebrew University of Jerusalem, (2022). https://www.docsity.com/en/what-is-eeg-elana-zion-golumbic/8925727/.
14- C. Q. Lai, H. Ibrahim, A. I. A. Hamid, and J. M. Abdullah, "Classification of non-severe traumatic brain injury from resting-state eeg signal using lstm network with ECOC-SVM," Sensors (Switzerland), vol. 20, no. 18, pp. 1–21, (2020), doi: 10.3390/s20185234.
15- C. Q. Lai, H. Ibrahim, J. M. Abdullah, A. Azman, and M. Z. Abdullah, "Convolutional Neural Network Utilizing Error-Correcting Output Codes Support Vector Machine for Classification of Non-Severe Traumatic Brain Injury from Electroencephalogram Signal," IEEE Access, vol. 9, pp. 24946–24964, (2021), doi: 10.1109/ACCESS.2021.3056724.
16- M. Vishwanath et al., "Classification of Electroencephalogram in a Mouse Model of Traumatic Brain Injury Using Machine Learning Approaches," Proc. Annu. Int. Conf. IEEE Eng. Med. Biol. Soc. EMBS, vol. 2020-July, pp. 3335–3338, (2020), doi: 10.1109/EMBC44109.2020.9175915.
17- M. Vishwanath et al., "Investigation of machine learning approaches for traumatic brain injury classification via eeg assessment in mice," Sensors (Switzerland), vol. 20, no. 7, (2020), doi: 10.3390/s20072027.
18- K. Thanjavur et al., "Recurrent neural network-based acute concussion classifier using raw resting state EEG data," Sci. Rep., vol. 11, no. 1, pp. 1–19, (2021), doi: 10.1038/s41598-021-91614-4.
19- A. Baddeley, "Working memory," Curr. Biol., vol. 20, no. 4, pp. 136–140, (2010), doi: 10.1016/j.cub.2009.12.014.
20- L. T. Hsieh and C. Ranganath, "Frontal midline theta oscillations during working memory maintenance and episodic encoding and retrieval," Neuroimage, vol. 85, pp. 721–729, (2014), doi: 10.1016/j.neuroimage.2013.08.003.
21- P. Klaver, E. Martin, L. Michels, K. Bucher, R. Lu, and D. Brandeis, "Simultaneous EEG-fMRI during a Working Memory Task : Modulations in Low and High Frequency Bands," vol. 5, no. 4, (2010), doi: 10.1371/journal.pone.0010298.
22- S. Raghavachari et al., "Gating of Human Theta Oscillations by a Working Memory Task," vol. 21, no. 9, pp. 3175–3183, (2001).
23- U. Maurer, S. Brem, and M. Liechti, "Frontal Midline Theta Reflects Individual Task Performance in a Working Memory Task," pp. 127–134, (2015), doi: 10.1007/s10548-014-0361-y.
24- M. Z. Zakrzewska and A. Brzezicka, "Working memory capacity as a moderator of load-related frontal midline theta variability in Sternberg task," vol. 8, no. June, pp. 1–7, (2014), doi: 10.3389/fnhum.2014.00399.
25- James F Cavanagh., “EEG: Visual Working Memory in Acute TBI,” OpenNeuro, (2022), doi: 10.18112/openneuro.ds003523.v1.1.0.
26- I. Güler and E. D. Übeyli, "Adaptive neuro-fuzzy inference system for classification of EEG signals using wavelet coefficients," J. Neurosci. Methods, vol. 148, no. 2, pp. 113–121, Oct. (2005), doi: 10.1016/j.jneumeth.2005.04.013.
27- S. G. Mallat, "A Theory for Multiresolution Signal Decomposition: The Wavelet Representation," IEEE Trans. Pattern Anal. Mach. Intell., vol. 11, no. 7, pp. 674–693, (1989), doi: 10.1109/34.192463.
28- P. Kaushik, A. Gupta, P. P. Roy, and D. P. Dogra, "EEG-Based Age and Gender Prediction Using Deep BLSTM-LSTM Network Model," IEEE Sens. J., vol. 19, no. 7, pp. 2634–2641, (2019), doi: 10.1109/JSEN.2018.2885582.
29- A. Alhudhaif, "A Novel Multi-class Imbalanced EEG Signals Classification Based on the Adaptive Synthetic Sampling (ADASYN) approach," PeerJ Comput. Sci., vol. 7, pp. 1–15, (2021), doi: 10.7717/PEERJ-CS.523.
30- G. Zhang, V. Davoodnia, A. Sepas-Moghaddam, Y. Zhang, and A. Etemad, "Classification of Hand Movements from EEG using a Deep Attention-based LSTM Network," (2019), doi: 10.1109/JSEN.2019.2956998.
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How to Cite
1.
Sahu P, Jain K. Frontal Medial Theta-Based Intelligence System for Verifying Mild Traumatic Brain Injury. Frontiers Biomed Technol. 2023;.
