Skip to main content
SpringerLink
Log in

The Probablistic Random Forest Clinico-Statistical Regression Analysis of MER Signals with STN-DBS and Enhancement of UPDRS

  • Conference paper
  • First Online:
World Congress on Medical Physics and Biomedical Engineering 2018

Part of the book series: IFMBE Proceedings ((IFMBE,volume 68/3))

  • 1893 Accesses

Abstract

In this study, we present classification and regression analysis to predict the UPDRS score and its enhancement after the microelectrode STN signal recording (MER) with DBS surgery (implantation of the microelectrode). We hypothesized that a data informed grouping of features extrapolated from MER signals of STN can envisage restore (by decreasing the tremor) and functioning the motor improvement in Parkinson’s disease (PD) patients. A random—forest is used to account for unbalanced datasets and multiple observations per PD subject, and showed that only five features of STN-MER signals are sufficient and account for prognosting UPDRS advancement. This finding suggests that STN signal characteristics are maximum correlated to the extent of improvement motor restoration and motor behavior observed in STN DBS.

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 259.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 329.99
Price excludes VAT (USA)
  • Compact, lightweight 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

Similar content being viewed by others

References

  1. Juliann, Schaeffer., New Hope in Neuroprotection? A Parkinson’s disease Update. Neuroprotection has become the focus of efforts aimed at slowing the progression of Parkinson`s disease, Nature.com 1–4 (2017).

    Google Scholar 

  2. Benabid, AL., Koudsie, A., Benazzouz, A., Vercueil, L., Fraix V., Chabardes, S., LeBas, JF., Pollak, P.: Deep brain stimulation of the corpus luysi (subthalamic nucleus) and other targets in Parkinson’s disease. Extension to new indications such as dystonia and epilepsy. J Neurol 248(3), III37–III47 (2001).

    Google Scholar 

  3. Benabid, AL., Krack, PP., Benazzouz, A., Limousin, P., Koudsie, A., Pollak, P.: Deep brain stimulation of the subthalamic nucleus for Parkinson’s disease: methodologic aspects and clinical criteria. Neurology 55:S40–S44 (2000).

    Google Scholar 

  4. J, Jankovic.: Parkinson’s disease: Clinical features and diagnosis. J. Neu-rol. Neurosurg. Psychiatry 79(4), 368–376 (2008).

    Google Scholar 

  5. Tugwell, C.: Parkinson’s disease in focus. Pharmaceutical Press, London (2008).

    Google Scholar 

  6. Yashar, Sarbaz., Hakimeh, Pourakbari.: A review of presented mathematical models in Parkinson’s disease: black and gray box models. Med Biol Eng Comput 54, 855–868 (2016).

    Google Scholar 

  7. Parkinson, J.: An Essay on Shaking Palsy, Sherwood, Neely and Jones: London (1817).

    Google Scholar 

  8. K, Ray Chaudhuri., Victor, SC Fung., Fast Facts: Parkinson`s Disease. Fourth Edition, Health Press Ltd. London (2016).

    Google Scholar 

  9. Ronald, F. Pfeiffer., Zbgkiew, K. Wazolek., Manuchair, Ebadi.: Parkinson`s Disease, CRC Press, Taylor & Francis Group (2013).

    Google Scholar 

  10. Alim, Luis Benabid.: Laskar Award Winner, Nature Medicine. 10 (20), 1–3 (2014).

    Google Scholar 

  11. Mahlon, DeLong.: Laskar Award Winner, Nature Medicine. 10 (20), 4–6 (2014).

    Google Scholar 

  12. Larry, Squire., Darwin, Berg., Floyd, E. Bloom., Sascha, du. Lac., Anirvan, Ghosh., Nicholas C. Spitzer.: Fundamental Neuroscience, 4th Edition. AP Academic Press (2012).

    Google Scholar 

  13. Duane E. Haines., Gregory A. Mihailoff.: Fundamental Neuroscience for Basic and Clinical Applications. E-Book 5th Edition. ELSEVIER (2018).

    Google Scholar 

  14. Ashkan, K., Blomstedt, P., Zrinzo, L., Tisch, S., Yousry, T., Limousin-Dowsey, P.: Variability of the subthalamic nucleus: the case for direct MRI guided targeting. Br J Neurosurg. 21 (2), 197–200 (2007).

    Google Scholar 

  15. Patel, NK., Khan, S., Gill, SS.: Comparison of atlas- and magnetic-resonance-imaging-based stereotactic targeting of the subthalamic nucleus in the surgical treatment of Parkinson’s disease. Stereotact Funct Neurosurg. 86(3), 153–61 (2008).

    Google Scholar 

  16. Andrade-Souza, YM., Schwalb, JM., Hamani, C., Eltahawy, H., Hoque, T., Saint-Cyr, J., Lozano, AM.: Comparison of three methods of targeting the subthalamic nucleus for chronic stimulation in Parkinson’s disease. Neurosurgery. 62 (2), 875–83 (2008).

    Google Scholar 

  17. B. W. Mathews.: Comparison of the predicted and observed secondary structure of T4 phage lysozyme. Biochim. Biophys. Acta. Protein Struct. 405(2), 442–451 (1975).

    Google Scholar 

  18. Parkinson J.: An Essay on Shaking Palsy, Sherwood, Neely and Jones: London (1817).

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Computer Science and Engineering, CMR College of Engineering & Technology (UGC Autonomous), Kandlakoya, Medchal Rd, 501401, Hyderabad, India

    Venkateshwarla Rama Raju

  2. Jawaharlal Nehru Technological University Hyderabad JNTUH, Hyderabad, India

    Venkateshwarla Rama Raju

  3. Nizam’s Institute of Medical Sciences, Biomedical, Neurology and Neurosurgery, Hyderabad, India

    Venkateshwarla Rama Raju

  4. Biomedical Engineering Department, Osmania University College of Engineering (Autonomous), Hyderabad, OU, India

    Venkateshwarla Rama Raju

Authors
  1. Venkateshwarla Rama Raju
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Venkateshwarla Rama Raju .

Editor information

Editors and Affiliations

  1. CIIRC, Czech Technical University in Prague, Prague, Czech Republic

    Lenka Lhotska

  2. Institute of Clinical and Experimental Medicine, Prague, Czech Republic

    Lucie Sukupova

  3. Faculty of Electrical Engineering and Computing, University of Zagreb, Zagreb, Croatia

    Igor Lacković

  4. Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA

    Geoffrey S. Ibbott

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer Nature Singapore Pte Ltd.

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Rama Raju, V. (2019). The Probablistic Random Forest Clinico-Statistical Regression Analysis of MER Signals with STN-DBS and Enhancement of UPDRS. In: Lhotska, L., Sukupova, L., Lacković, I., Ibbott, G. (eds) World Congress on Medical Physics and Biomedical Engineering 2018. IFMBE Proceedings, vol 68/3. Springer, Singapore. https://doi.org/10.1007/978-981-10-9023-3_9

Download citation

  • DOI: https://doi.org/10.1007/978-981-10-9023-3_9

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-10-9022-6

  • Online ISBN: 978-981-10-9023-3

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation