Skip to main content
SpringerLink
Log in

An Artificial Intelligence Based Effective Diagnosis of Parkinson Disease Using EEG Signal

  • Chapter
  • First Online:
Data Analysis for Neurodegenerative Disorders

Part of the book series: Cognitive Technologies ((COGTECH))

  • 387 Accesses

Abstract

This study focuses on the use of human bio-signals for the early diagnosis of PD (Parkinson’s disease). EEG (Electroencephalography) and EMG have been used to examine human brain and muscle signals to learn more about the functional and neurological alterations of Parkinson’s patients. Parkinson disease (PD) is a neurological illness that typically affects people over the age of 50. Dopamine, a neurotransmitter, is depleted in the substantia nigra as a result. As this neurotransmitter is released, the person’s muscles begin to contract. Reduced dopamine production causes a loss of brain and muscle coordination, which manifests as unsteady limb movement in a person with PD. The underlying aetiology of PD can be validated by studying the functional and neural alterations using EEG and correlating the results with EMG. It will explain the origin of the wide range of early-stage motor and non-motor PD symptoms. The EEG and EMG results for detecting early-stage PD were validated using other radiological data, such as a Brain Magnetic Imaging signal. The mathematical model for PD diagnosis was developed utilising an ANN and a graphical user interface. The ANN-designed classifier achieved a near-perfect accuracy rate of 100% while testing its ability to distinguish between an early-stage PD patient and a control subject using a dataset consisting of electroencephalogram and electromyogram readings as input features.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 169.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Hardcover Book
USD 219.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Yuvaraj, R., Acharya, U.R., Hagiwara, Y.: A novel PD diagnosis index using higher-order spectra features in EEG signals. Neural Comput. Appl. 30(4), 1225–1235 (2018)

    Article  Google Scholar 

  2. Yuvaraj, R., Murugappan, M., Acharya, U.R., Adeli, H., Ibrahim, N.M., Mesquita, E.: Brain functional connectivity patterns for emotional state classification in Parkinson’s disease patients without dementia. Behav. Brain Res. 298, 248–260 (2016)

    Article  Google Scholar 

  3. Yuvaraj, R., Murugappan, M., Ibrahim, N.M., Omar, M.I., Sundaraj, K., Mohamad, K., Satiyan, M.: On the analysis of EEG power, frequency and asymmetry in PD during emotion processing. Behav. Brain Funct. 10(1), 12 (2014)

    Article  Google Scholar 

  4. Wang, G., Shepherd, S.J., Beggs, C.B., Rao, N., Zhang, Y.: The use of kurtosis denoising for EEG analysis of patients suffering from Alzheimer’s disease. Bio-Med. Mater. Eng. 26(s1), S1135–S1148 (2015)

    Article  Google Scholar 

  5. Wang, K.-L., Burns, M., Xu, D., Hu, W., Fan, S.-Y., Han, C.-L., Zhang, J.-G.: Electromyography biomarkers for quantifying the intraoperative efficacy of deep brain stimulation in Parkinson’s patients with resting tremor. Front. Neurol. 11 (2020)

    Google Scholar 

  6. Urigüen, J.A., Garcia-Zapirain, B.: EEG artifact removal—state-of-the-art and guidelines. J. Neural Eng. 12(3), 031001 (2015)

    Article  Google Scholar 

  7. Vanegas, M.I., Ghilardi, M.F., Kelly, S.P., Blangero, A.: Machine learning for EEG-based biomarkers in Parkinson’s disease. Presented at the 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) (2018)

    Google Scholar 

  8. Venuto, D.D., Annese, V.F., Defazio, G., Gallo, V.L., Mezzina, G.: Gait analysis and quantitative drug effect evaluation in Parkinson disease by jointly EEGEMG monitoring. Presented at the 2017 12th International Conference on Design & Technology of Integrated Systems in Nanoscale Era (DTIS) (2017)

    Google Scholar 

  9. Um, T.T., Pfister, F.M.J., Pichler, D., Endo, S., Lang, M., Hirche, S., Kulić, D.: Data augmentation of wearable sensor data for PD monitoring using convolutional neural networks. arXiv:1706.00527 (2017)

  10. Thilakvathi, B., Devi, S.S., Bhanu, K., Malaippan, M.: EEG signal complexity analysis for schizophrenia during rest and mental activity. Biomed. Res. 28(1), 1–9 (2017)

    Google Scholar 

  11. Tuncer, T., Dogan, S., Acharya, U.R.: Automated detection of PD using minimum average maximum tree and singular value decomposition method with vowels. Biocybern. Biomed. Eng. 40(1), 211–220 (2020)

    Article  Google Scholar 

  12. Stefanis, L., de Andrade, J.B.C., Mohr, J.P.: Brain arteriovenous malformation and amyotrophic lateral sclerosis: a review based on published cases. SN Comprehensive Clin. Med. 1–5 (2020)

    Google Scholar 

  13. Alam Khan, Z., Feng, Z., Uddin, M.I., Mast, N., Ali Shah, S.A., Imtiaz, M., Mahmoud, M.: Optimal policy learning for disease prevention using reinforcement learning. Sci. Program. 2020, 1–13 (2020)

    Google Scholar 

  14. Hussain, M., Koundal, D., Manhas, J.: Deep learning-based diagnosis of disc degenerative diseases using MRI: a comprehensive review. Comput. Electr. Eng. 105, 108524 (2023)

    Article  Google Scholar 

  15. Segura-Aguilar, J., Paris, I., Muñoz, P., Ferrari, E., Zecca, L., Zucca, F.A.: Protective and toxic roles of dopamine in Parkinson’s disease. J. Neurochem. 129(6), 898–915 (2014)

    Article  Google Scholar 

  16. Ruonala, V., Pekkonen, E., Airaksinen, O., Kankaanpää, M., Karjalainen, P.A., & Rissanen, S.M.: Levodopa-induced changes in electromyographic patterns in patients with advanced Parkinson’s disease. Front. Neurol. 9(35). https://doi.org/10.3389/fneur.2018.00035 (2018)

  17. Santos‐García, D., de Deus Fonticoba, T., Castro, E.S., Díaz, A.A., McAfee, D., Catalán, M., Mir, P.: Non‐motor symptoms burden is strongly correlated to motor complications in Parkinson’s disease patients. Euro. J. Neurol. (2020)

    Google Scholar 

  18. Ricciardi, C., Amboni, M., De Santis, C., Ricciardelli, G., Improta, G., Iuppariello, L., Cesarelli, M.: Classifying different stages of PD through random forests. Presented at the Mediterranean Conference on Medical and Biological Engineering and Computing (2019)

    Google Scholar 

  19. Qin, Z., Jiang, Z., Chen, J., Hu, C., Ma, Y.: sEMG-based tremor severity evaluation for PD using a light-weight CNN. IEEE Signal Process. Lett. 26(4), 637–641 (2019). https://doi.org/10.1109/LSP.2019.2903334

  20. Raethjen, J., Govindan, R., Muthuraman, M., Kopper, F., Volkmann, J., Deuschl, G.: Cortical correlates of the basic and first harmonic frequency of Parkinsonian tremor. Clin. Neurophysiol. 120(10), 1866–1872 (2009)

    Article  Google Scholar 

  21. Aktürk, T., Yıldırım, E., Hanoğlu, L., Yılmaz, N.H., Yener, G.G., Güntekin, B.: A longitudinal investigation of event related EEG brain oscillations in patients with Parkinson’s disease. Anatomy Int. J. Exper. Clin. Anatomy 13 (2019)

    Google Scholar 

  22. Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of EEG signal features extraction using linear analysis in frequency and time-frequency domains. ISRN Neurosci. (2014)

    Google Scholar 

  23. Camara, C., Isasi, P., Warwick, K., Ruiz, V., Aziz, T., Stein, J., Bakštein, E.: Resting tremor classification and detection in PD patients. Biomed. Signal Process. Control 16, 88–97 (2014)

    Article  Google Scholar 

  24. Chakraborty, S., Aich, S., Sim, J.-S., Jang, D.-J., Joo, M.-I., Kim, H.-C.: Detection of neurodegenerative disease in ageing adults: a systematic review. TEST Eng. Manage. 82, 8982–8990 (2020)

    Google Scholar 

  25. Chaudhuri, K.R., Schapira, A.H.: Non-motor symptoms of Parkinson’s disease: dopaminergic pathophysiology and treatment. Lancet Neurol. 8(5), 464–474 (2009)

    Article  Google Scholar 

  26. Chen, X., Wang, Z.J., McKeown, M.J.: A three-step multimodal analysis framework for modeling corticomuscular activity with application to Parkinson’s disease. IEEE J. Biomed. Health Inform. 18(4), 1232–1241 (2014). https://doi.org/10.1109/JBHI.2013.2284480

    Article  Google Scholar 

  27. Chiang, J., Wang, Z.J., McKeown, M.J.: A multiblock PLS model of cortico-cortical and corticomuscular interactions in Parkinson’s disease. Neuroimage 63(3), 1498–1509 (2012)

    Article  Google Scholar 

  28. Chu, C., Wang, X., Cai, L., Zhang, L., Wang, J., Liu, C., Zhu, X.: Spatiotemporal EEG microstate analysis in drug-free patients with Parkinson’s disease. NeuroImage Clini. 25, 102132 (2020)

    Google Scholar 

  29. Cockrell, J.R., Folstein, M.F.: Mini-mental state examination. Principles and practice of geriatric psychiatry, 140–141 (2002)

    Google Scholar 

  30. Feng, Y., Liu, Y., Liu, Z., Liu, W., Yao, Q., Zhang, X.: A novel interval iterative multi-thresholding algorithm based on hybrid spatial filter and region growing for medical brain MR images. Appl. Sci. 13(2), 1087 (2023)

    Article  Google Scholar 

  31. Dong, M., Husain, M., Brooks, D., Wilson, M., Craven, M., Destrebecq, F., Aps, B.: Alzheimer’s disease (ad) detect and prevent-presymptomatic ad detection and prevention, (2020)

    Google Scholar 

  32. Dorszewska, J.: Genetic factors in Parkinson’s disease. Curr. Genomics 14(8), 485 (2020)

    Article  Google Scholar 

  33. Duda, R.O., Hart, P.E., Stork, D.G.: Pattern Classification. John Wiley & Sons (2012)

    Google Scholar 

  34. Salankar, N., Koundal, D., Chakraborty, C., Garg, L.: Automated attention deficit classification system from multimodal physiological signals. Multimedia Tools Appl. 1–16 (2022)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. School of Information Technology, Skyline University College, University City Sharjah, 1797, Sharjah, UAE

    Mahmoud Ahmad Al-Khasawneh

  2. College of Computer Science and Engineering, University of Jeddah, Jeddah, 21959, Saudi Arabia

    Abdulrahman Alzahrani & Alaa Alarood

Authors
  1. Mahmoud Ahmad Al-Khasawneh
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Abdulrahman Alzahrani
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Alaa Alarood
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Mahmoud Ahmad Al-Khasawneh .

Editor information

Editors and Affiliations

  1. School of Computer Science, University of Petroleum and Energy Studies, Dehradun, Uttarakhand, India

    Deepika Koundal

  2. Chongqing University of Posts and Telecommunications, Chongqing, China

    Deepak Kumar Jain

  3. Department of Computer Science, University of Illinois at Springfield, Springfield, IL, USA

    Yanhui Guo

  4. Faculty of Engineering, Tanta University, Tanta, Al-Garbia, Egypt

    Amira S. Ashour

  5. Department of Computer Science, Taif University, Taif, Saudi Arabia

    Atef Zaguia

Rights and permissions

Reprints and permissions

Copyright information

© 2023 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Al-Khasawneh, M.A., Alzahrani, A., Alarood, A. (2023). An Artificial Intelligence Based Effective Diagnosis of Parkinson Disease Using EEG Signal. In: Koundal, D., Jain, D.K., Guo, Y., Ashour, A.S., Zaguia, A. (eds) Data Analysis for Neurodegenerative Disorders. Cognitive Technologies. Springer, Singapore. https://doi.org/10.1007/978-981-99-2154-6_14

Download citation

  • DOI: https://doi.org/10.1007/978-981-99-2154-6_14

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-99-2153-9

  • Online ISBN: 978-981-99-2154-6

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation