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Alcoholic EEG Data Classification Using Weighted Graph-Based Technique

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Health Information Science (HIS 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13705))

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Abstract

The analysis and classification of Alcohol Use Disorder (AUD) using non-invasive measurements, such as EEG records from the brain scalp, are of significant importance in neuroscience. Analysis and diagnosis of brain diseases associated with alcoholic subjects using EEG records remain challenging. This study proposes a graph theory-based approach for automated classification of AUD using EEG data. The metrics of the graphs are intrinsically related to the organization of the brain functionality. The main contribution of this study is to evaluate the impact of weighted graph features on AUD classifications based on EEG data. In this study, three different features (average degree, fluctuation difference, and average weighted degree) were extracted from the weighed visibility EEG graph and the performance of the proposed model was evaluated against SVM, k-NN, and Naive Bayes classifiers. The experimental results indicates that the topological features of the weighted EEG graphs supported superior classification performance (97.5%) against the other competing methods.

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References

  1. Diagnostic and Statistical Manual of Mental Disorders: DSM-5, 5th edn. Reference Reviews 28, 36–37 (2014)

    Google Scholar 

  2. Understanding Alcohol Use Disorder | National Institute on Alcohol Abuse and Alcoholism (NIAAA). https://www.niaaa.nih.gov/publications/brochures-and-fact-sheets/understanding-alcohol-use-disorder

  3. Alcohol. https://www.who.int/news-room/fact-sheets/detail/alcohol#:~:text=The%20harmful%20use%20of%20alcohol,represents%205.3%25%20of%20all%20deaths

  4. Sadiq, M., Akbari, H., Siuly, S., Li, Y., Wen, P.: Alcoholic EEG signals recognition based on phase space dynamic and geometrical features. Chaos Solitons Fractals 158, 112036 (2022)

    Google Scholar 

  5. Hussain, W., Sadiq, M., Siuly, S., Rehman, A.: Epileptic seizure detection using 1 D-convolutional long short-term memory neural networks. Appl. Acoust. 177, 107941 (2021)

    Article  Google Scholar 

  6. Şengür, D., Siuly, S.: Efficient approach for EEG-based emotion recognition. Electron. Lett. 56, 1361–1364 (2020)

    Article  Google Scholar 

  7. Namura, I.: Alcoholic brain damage and dementia viewed by MRI, with special consideration on frontal atrophy and white matter damage in dyslipidemic patients. Psychogeriatrics 6, 119–127 (2006)

    Article  Google Scholar 

  8. Pfefferbaum, A., Sullivan, E.: Disruption of brain white matter microstructure by excessive intracellular and extracellular fluid in alcoholism: evidence from diffusion tensor imaging. Neuropsychopharmacology 30, 423–432 (2004)

    Article  Google Scholar 

  9. Solingapuram Sai, K., Hurley, R., Dodda, M., Taber, K.: Positron emission tomography: updates on imaging of addiction. J. Neuropsychiatry Clin. Neurosci. 31, A6-288 (2019)

    Article  Google Scholar 

  10. Supriya, S., Siuly, S., Wang, H., Zhang, Y.: An efficient framework for the analysis of big brain signals data. In: Wang, J., Cong, G., Chen, J., Qi, J. (eds.) Databases Theory and Applications, vol. 10837, pp. 199–207. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-92013-9_16

    Chapter  Google Scholar 

  11. Farsi, L., Siuly, S., Kabir, E., Wang, H.: Classification of alcoholic EEG signals using a deep learning method. IEEE Sens. J. 21, 3552–3560 (2021)

    Article  Google Scholar 

  12. Bajaj, V., Guo, Y., Sengur, A., Siuly, S., Alcin, O.F.: A hybrid method based on time–frequency images for classification of alcohol and control EEG signals. Neural Comput. Appl. 28(12), 3717–3723 (2016). https://doi.org/10.1007/s00521-016-2276-x

    Article  Google Scholar 

  13. Faust, O., Yu, W., Kadri, N.: Computer-based identification of normal and alcoholic EEG signals using wavelet packets and energy measures. J. Mech. Med. Biol. 13, 1350033 (2013)

    Google Scholar 

  14. Supriya, S., Siuly, S., Wang, H., Zhang, Y.: New feature extraction for automated detection of epileptic seizure using complex network framework. Appl. Acoust. 180, 108098 (2021)

    Article  Google Scholar 

  15. Smit, D., Stam, C., Posthuma, D., Boomsma, D., de Geus, E.: Heritability of “small-world” networks in the brain: a graph theoretical analysis of resting-state EEG functional connectivity. Hum. Brain Mapp. 29, 1368–1378 (2008)

    Article  Google Scholar 

  16. Supriya, S., Siuly, S., Wang, H., Zhang, Y.: Epilepsy detection from EEG using complex network techniques: a review. IEEE Rev. Biomed. Eng. 1 (2021)

    Google Scholar 

  17. Sporns, O.: Graph theory methods for the analysis of neural connectivity patterns. In: Kötter, R. (ed.) Neuroscience Databases, pp. 171–185. Springer, Boston (2003)

    Chapter  Google Scholar 

  18. Supriya, S., Siuly, S., Wang, H., Cao, J., Zhang, Y.: Weighted visibility graph with complex network features in the detection of epilepsy. IEEE Access. 4, 6554–6566 (2016)

    Article  Google Scholar 

  19. Supriya, S., Siuly, S., Zhang, Y.: Automatic epilepsy detection from EEG introducing a new edge weight method in the complex network. Electron. Lett. 52, 1430–1432 (2016)

    Article  Google Scholar 

  20. Supriya, S., Siuly, S., Wang, H., Zhang, Y.: EEG sleep stages analysis and classification based on weighed complex network features. IEEE Trans. Emerging Top. Comput. Intell. 5, 236–246 (2021)

    Article  Google Scholar 

  21. Supriya, S., Siuly, S., Wang, H., Zhang, Y.: Automated epilepsy detection techniques from electroencephalogram signals: a review study. Health Inf. Sci. Syst. 8(1), 1–15 (2020). https://doi.org/10.1007/s13755-020-00129-1

    Article  Google Scholar 

  22. Cao, R., Wu, Z., Li, H., Xiang, J., Chen, J.: Disturbed connectivity of EEG functional networks in alcoholism: a graph-theoretic analysis. Bio-Med. Mater. Eng. 24, 2927–2936 (2014)

    Article  Google Scholar 

  23. Zhu, G., Li, Y., Wen, P., Wang, S.: Analysis of alcoholic EEG signals based on horizontal visibility graph entropy. Brain Inform. 1(1–4), 19–25 (2014). https://doi.org/10.1007/s40708-014-0003-x

    Article  Google Scholar 

  24. Hassan, A., Siuly, S., Zhang, Y.: Epileptic seizure detection in EEG signals using tunable-Q factor wavelet transform and bootstrap aggregating. Comput. Methods Programs Biomed. 137, 247–259 (2016)

    Article  Google Scholar 

  25. Siuly, S., Kabir, E., Wang, H., Zhang, Y.: Exploring sampling in the detection of multicategory EEG signals. Comput. Math. Methods Med. 2015, 1–12 (2015)

    Article  Google Scholar 

  26. Lacasa, L., Luque, B., Ballesteros, F., Luque, J., Nuño, J.: From time series to complex networks: the visibility graph. Proc. Natl. Acad. Sci. 105, 4972–4975 (2008)

    Article  MathSciNet  Google Scholar 

  27. Wu, J., Sun, L., Peng, D., Siuly, S.: A micro neural network for healthcare sensor data stream classification in sustainable and smart cities. Comput. Intell. Neurosci. 2022, 1–9 (2022)

    Google Scholar 

  28. Supriya, S., Wang, H., Zhuo, G., Zhang, Y.: Analyzing EEG signal data for detection of epileptic seizure: introducing weight on visibility graph with complex network feature. In: Cheema, M., Zhang, W., Chang, L. (eds.) Databases Theory and Applications, vol. 9877, pp. 56–66. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46922-5_5

    Chapter  Google Scholar 

  29. Alvi, A., Siuly, S., Wang, H.: A long short-term memory based framework for early detection of mild cognitive impairment from EEG signals. IEEE Trans. Emerging Top. Comput. Intell. 1–14 (2022)

    Google Scholar 

  30. Sadiq, M.T., Siuly, S., Ur Rehman, A., Wang, H.: Auto-correlation based feature extraction approach for EEG alcoholism identification. In: Siuly, S., Wang, H., Chen, L., Guo, Y., Xing, C. (eds.) HIS 2021. LNCS, vol. 13079, pp. 47–58. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-90885-0_5

    Chapter  Google Scholar 

  31. Antoniou, I., Tsompa, E.: Statistical analysis of weighted networks. Discret. Dyn. Nat. Soc. 2008, 1–16 (2008)

    Article  MathSciNet  Google Scholar 

  32. Jiang, W.: Time series classification: nearest neighbor versus deep learning models. SN Appl. Sci. 2(4), 1–17 (2020). https://doi.org/10.1007/s42452-020-2506-9

    Article  Google Scholar 

  33. Wen-ge, F.: Application of SVM classifier in IR target recognition. Phys. Procedia 24, 2138–2142 (2012)

    Article  Google Scholar 

  34. Machado, J., Balbinot, A.: Executed movement using EEG signals through a naive Bayes classifier. Micromachines 5, 1082–1105 (2014)

    Article  Google Scholar 

  35. EEG Database. http://kdd.ics.uci.edu/databases/eeg/eeg.data.html

  36. Zhang, X., Begleiter, H., Porjesz, B., Wang, W., Litke, A.: Event related potentials during object recognition tasks. Brain Res. Bull. 38, 531–538 (1995)

    Article  Google Scholar 

  37. Tang, X., et al.: New approach to epileptic diagnosis using visibility graph of high-frequency signal. Clin. EEG Neurosci. 44, 150–156 (2013)

    Article  Google Scholar 

  38. Kannathal, N., Acharya, U., Lim, C., Sadasivan, P.: Characterization of EEG—a comparative study. Comput. Methods Programs Biomed. 80, 17–23 (2005)

    Article  Google Scholar 

  39. Ehlers, C., Havstad, J., Prichard, D., Theiler, J.: Low doses of ethanol reduce evidence for nonlinear structure in brain activity. J. Neurosci. 18, 7474–7486 (1998)

    Article  Google Scholar 

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Author information

Authors and Affiliations

  1. Torrens University, Flinders Street, Melbourne, VIC, 3000, Australia

    Supriya Supriya, Tony Jan, Nandini Sidnal & Scott Thompson-Whiteside

Authors
  1. Supriya Supriya
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  2. Tony Jan
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  3. Nandini Sidnal
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  4. Scott Thompson-Whiteside
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Corresponding author

Correspondence to Supriya Supriya .

Editor information

Editors and Affiliations

  1. University of São Paulo, São Carlos, Brazil

    Agma Traina

  2. Victoria University, Melbourne, VIC, Australia

    Hua Wang

  3. Tsinghua University, Beijing, China

    Yong Zhang

  4. Victoria University, Footscray, VIC, Australia

    Siuly Siuly

  5. Swinburne University of Technology, Hawthorn, VIC, Australia

    Rui Zhou

  6. Swinburne University of Technology, Hawthorn, VIC, Australia

    Lu Chen

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Supriya, S., Jan, T., Sidnal, N., Thompson-Whiteside, S. (2022). Alcoholic EEG Data Classification Using Weighted Graph-Based Technique. In: Traina, A., Wang, H., Zhang, Y., Siuly, S., Zhou, R., Chen, L. (eds) Health Information Science. HIS 2022. Lecture Notes in Computer Science, vol 13705. Springer, Cham. https://doi.org/10.1007/978-3-031-20627-6_25

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  • DOI: https://doi.org/10.1007/978-3-031-20627-6_25

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-20626-9

  • Online ISBN: 978-3-031-20627-6

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