▷ Classification of Subjects with Parkinson's Disease using Finger Tapping Dataset


✅ Video of the talk:

✅ Conference content:



  • ➡️ Introduction
  • ➡️ Related Work
  • ➡️ Dataset

  • ➡️ Methodology






  • ➡️ Results and Discussion

  • ➡️ Conclusions


✅ References:
    1.  Abedin, M. M., Maniruzzaman, M., Ahmed, N. F., Ahammed, B., & Ali, M. (2019, December). Classification and prediction of parkinson disease: A machine learning approach. In International Conference Data Science and SDGs: Challenges, Opportunities and Realities.
    2. Asanza, V., Sanchez, G., Cajo, R., & Peláez, E. (2020, July). Behavioral signal processing with machine learning based on fpga. In International Conference on Systems and Information Sciences (pp. 196-207). Springer, Cham.
    3. Bhatti, D., Thompson, R., Hellman, A., Penke, C., Bertoni, J. M., & Torres‐Russotto, D. (2017). Smartphone apps provide a simple, accurate bedside screening tool for orthostatic tremor. Movement disorders clinical practice, 4(6), 852-857.
    4. Brown, E. G., & Goldman, S. M. (2020). Modulation of the microbiome in Parkinson’s disease: diet, drug, stool transplant, and beyond. Neurotherapeutics, 17(4), 1406-1417.
    5. Chen, Y., Zhu, G., Liu, D., Liu, Y., Yuan, T., Zhang, X., ... & Zhang, J. (2020). The morphology of thalamic subnuclei in Parkinson's disease and the effects of machine learning on disease diagnosis and clinical evaluation. Journal of the neurological sciences, 411, 116721.
    6. Chun, P. J., Izumi, S., & Yamane, T. (2021). Automatic detection method of cracks from concrete surface imagery using two‐step light gradient boosting machine. Computer‐Aided Civil and Infrastructure Engineering, 36(1), 61-72.
    7. Farahani, B., Firouzi, F., Chang, V., Badaroglu, M., Constant, N., & Mankodiya, K. (2018). Towards fog-driven IoT eHealth: Promises and challenges of IoT in medicine and healthcare. Future Generation Computer Systems, 78, 659-676.
    8. Germine, L., Strong, R. W., Singh, S., & Sliwinski, M. J. (2021). Toward dynamic phenotypes and the scalable measurement of human behavior. Neuropsychopharmacology, 46(1), 209-216.
    9. Goldberger, A. L., Amaral, L. A., Glass, L., Hausdorff, J. M., Ivanov, P. C., Mark, R. G., ... & Stanley, H. E. (2000). PhysioBank, PhysioToolkit, and PhysioNet: components of a new research resource for complex physiologic signals. circulation, 101(23), e215-e220.
    10. Hariharan, M., Polat, K., & Sindhu, R. (2014). A new hybrid intelligent system for accurate detection of Parkinson's disease. Computer methods and programs in biomedicine, 113(3), 904-913.
    11. Karabayir, I., Goldman, S. M., Pappu, S., & Akbilgic, O. (2020). Gradient boosting for Parkinson’s disease diagnosis from voice recordings. BMC Medical Informatics and Decision Making, 20(1), 1-7.
    12. Lau, A. Y., & Staccini, P. (2019). Artificial intelligence in health: new opportunities, challenges, and practical implications. Yearbook of medical informatics, 28(01), 174-178.
    13. Maccarrone, M. (2020). Missing pieces to the endocannabinoid puzzle. Trends in molecular medicine, 26(3), 263-272.
    14. Pereira, C. R., Pereira, D. R., Rosa, G. H., Albuquerque, V. H., Weber, S. A., Hook, C., & Papa, J. P. (2018). Handwritten dynamics assessment through convolutional neural networks: An application to Parkinson's disease identification. Artificial intelligence in medicine, 87, 67-77.
    15. Rodríguez-Cruz, A., Romo-Mancillas, A., Mendiola-Precoma, J., Escobar-Cabrera, J. E., García-Alcocer, G., & Berumen, L. C. (2020). Effect of valerenic acid on neuroinflammation in a MPTP-induced mouse model of Parkinson’s disease. IBRO reports, 8, 28-35.
    16. Sadek, R. M., Mohammed, S. A., Abunbehan, A. R. K., Ghattas, A. K. H. A., Badawi, M. R., Mortaja, M. N., ... & Abu-Naser, S. S. (2019). Parkinson's disease prediction using artificial neural network.
    17. Shah, S. A. A., Zhang, L., & Bais, A. (2020). Dynamical system based compact deep hybrid network for classification of Parkinson disease related EEG signals. Neural Networks, 130, 75-84.
    18. Shtar, G., Rokach, L., Shapira, B., Nissan, R., & Hershkovitz, A. (2021). Using machine learning to predict rehabilitation outcomes in postacute hip fracture patients. Archives of physical medicine and rehabilitation, 102(3), 386-394.
    19. Singh, A., Prakash, B. S., & Chandrasekaran, K. (2016, April). A comparison of linear discriminant analysis and ridge classifier on Twitter data. In 2016 International Conference on Computing, Communication and Automation (ICCCA) (pp. 133-138). IEEE.
    20. Sveinbjornsdottir, S. (2016). The clinical symptoms of Parkinson's disease. Journal of neurochemistry, 139, 318-324.
    21. Urcuqui, C., Castaño, Y., Delgado, J., Navarro, A., Diaz, J., Muñoz, B., & Orozco, J. (2018, September). Exploring Machine Learning to Analyze Parkinson's Disease Patients. In 2018 14th International Conference on Semantics, Knowledge and Grids (SKG) (pp. 160-166). IEEE.
    22. Váradi, C. (2020). Clinical features of Parkinson’s disease: the evolution of critical symptoms. Biology, 9(5), 103.
    23. Wang, T., Zhang, D., Wang, Z., Jia, J., Ni, H., & Zhou, X. (2015, August). Recognizing gait pattern of Parkinson's disease patients based on fine-grained movement function features. In 2015 IEEE 12th Intl Conf on Ubiquitous Intelligence and Computing and 2015 IEEE 12th Intl Conf on Autonomic and Trusted Computing and 2015 IEEE 15th Intl Conf on Scalable Computing and Communications and Its Associated Workshops (UIC-ATC-ScalCom) (pp. 1-10). IEEE.
    24. Wroge, T. J., Özkanca, Y., Demiroglu, C., Si, D., Atkins, D. C., & Ghomi, R. H. (2018, December). Parkinson’s disease diagnosis using machine learning and voice. In 2018 IEEE signal processing in medicine and biology symposium (SPMB) (pp. 1-7). IEEE.

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