Parkinson’s Disease Detection by Using Feature Selection and Sparse Representation


Parkinson’s disease is one of the most destructive diseases of the nervous system, affecting sound faster and more than any other subsystem of the body. Over the past decade, researchers have studied Parkinson’s disease by analyzing audio signals. It is a low-cost method that eliminates the need for the patient to be physically present at the clinic. By recording the sound signal from the pronunciation of the words and then extracting the appropriate features from them, it is possible to identify the disturbance in the sound movements of the person with Parkinson’s. Therefore, the ability to diagnose the disease before other clinical symptoms will be available. This paper examines the disability caused by Parkinson’s voice disorder and extracts parameters from the audio signal that well illustrate the disabilities in the voice. The proposed method uses a sparse representation algorithm to reduce the dimensions of the feature. At this stage, the best features are extracted from the person’s voice and appearance. Then, the resulting properties are sent as input to the sparse code classifiers. The accuracy of the proposed method for approximate message passing (AMP) classifiers is 99.11%. Evaluation of the proposed method shows that it has reduced storage space, in addition to increasing the efficiency of detection compared to the other methods.

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  1. 1.

    Altay, E. V., & Alatas, B. (2020). Association analysis of Parkinson’s disease with vocal change characteristics using multi-objective metaheuristic optimization. Medical Hypotheses, 141, 109722.

    Article  Google Scholar 

  2. 2.

    Oung, Q. W., et al. (2015). Technologies for assessment of motor disorders in Parkinson’s disease: A review. Sensors, 15(9), 21710–21745.

    Article  Google Scholar 

  3. 3.

    Tsanas, A. (2019). New insights into Parkinson’s disease through statistical analysis of standard clinical scales quantifying symptom severity. In 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).

  4. 4.

    Lahmiri, S., & Shmuel, A. (2019). Detection of Parkinson’s disease based on voice patterns ranking and optimized support vector machine. Biomedical Signal Processing and Control, 49, 427–433.

    Article  Google Scholar 

  5. 5.

    Chen, Y. S., et al. (2020). Identification of the framingham risk score by an entropy-based rule model for cardiovascular disease. Entropy, 22(12), 1406.

    CAS  Article  Google Scholar 

  6. 6.

    Chen, H. L., et al. (2016). An efficient hybrid kernel extreme learning machine approach for early diagnosis of Parkinson’s disease. Neurocomputing, 184, 131–144.

    Article  Google Scholar 

  7. 7.

    Emamzadeh, F. N., & Surguchov, A. (2018). Parkinson’s disease: Biomarkers, treatment, and risk factors. Frontiers in Neuroscience, 12, 612.

    Article  Google Scholar 

  8. 8.

    Shahbakhi, M., Far, D. T., & Tahami, E. (2014). Speech analysis for diagnosis of parkinson’s disease using genetic algorithm and support vector machine. Journal of Biomedical Science and Engineering, 7(4), 147–156.

    Article  Google Scholar 

  9. 9.

    Avci, D., & Dogantekin, A. (2016). An expert diagnosis system for parkinson disease based on genetic algorithm-wavelet kernel-extreme learning machine. Parkinson’s Disease, 2016, 5264743.

    PubMed  PubMed Central  Google Scholar 

  10. 10.

    Zhang, H. H., et al. (2016). Classification of Parkinson’s disease utilizing multi-edit nearest-neighbor and ensemble learning algorithms with speech samples. Biomedical Engineering Online, 15(1), 122.

    Article  Google Scholar 

  11. 11.

    Gil-Martín, M., Montero, J. M., & San-Segundo, R. (2019). Parkinson’s disease detection from drawing movements using convolutional neural networks. Electronics, 8(8), 907.

    Article  Google Scholar 

  12. 12.

    Mostafa, S. A., et al. (2019). Examining multiple feature evaluation and classification methods for improving the diagnosis of Parkinson’s disease. Cognitive Systems Research, 54, 90–99.

    Article  Google Scholar 

  13. 13.

    Singh, M., & Pati, D. (2019). Combining evidences from Hilbert envelope and residual phase for detecting replay attacks. International Journal of Speech Technology, 22(2), 313–326.

    Article  Google Scholar 

  14. 14.

    Kadam, V. J., & Jadhav, S. M. (2019). Feature ensemble learning based on sparse autoencoders for diagnosis of Parkinson’s disease. Computing Communication and Signal Processing, 810, 567–581.

    Article  Google Scholar 

  15. 15.

    Farsi, H., Nasiripour, R., & Mohammadzadeh, S. (2018). Eye gaze detection based on learning automata by using SURF descriptor.Information Systems & Telecommunication, 41.

  16. 16.

    Liu, Y., Liu, S., & Wang, Z. (2015). A general framework for image fusion based on multi-scale transform and sparse representation. Information Fusion, 24, 147–164.

    Article  Google Scholar 

  17. 17.

    Jiao, J., Zhao, M., Lin, J., & Liang, K. (2019). Hierarchical discriminating sparse coding for weak fault feature extraction of rolling bearings. Reliability Engineering & System Safety, 184, 41–54.

    Article  Google Scholar 

  18. 18.

    Nasiripour, R., Farsi, H., & Mohamadzadeh, S. (2019). Visual saliency object detection using sparse learning. IET Image Processing, 13(13), 2436–2447.

    Article  Google Scholar 

  19. 19.

    Sezavar, A., Farsi, H., & Mohamadzadeh, S. (2019). Content-based image retrieval by combining convolutional neural networks and sparse representation. Multimedia Tools and Applications, 78(15), 20895–20912.

    Article  Google Scholar 

  20. 20.

    Little, M., et al. (2008). Suitability of dysphonia measurements for telemonitoring of Parkinson’s disease. Nature Precedings.

  21. 21.

    Zham, P., et al. (2017). Efficacy of guided spiral drawing in the classification of Parkinson’s disease. IEEE Journal of Biomedical and Health Informatics, 22(5), 1648–1652.

    Article  Google Scholar 

  22. 22.

    Kotsavasiloglou, C., et al. (2017). Machine learning-based classification of simple drawing movements in Parkinson’s disease. Biomedical Signal Processing and Control, 31, 74–180.

    Article  Google Scholar 

  23. 23.

    Gallicchio, C., Micheli, A., Pedrelli, L. (2018). Deep echo state networks for diagnosis of parkinson’s disease. arXiv:1802.06708

  24. 24.

    Khatamino, P., Cantürk, İ., & Özyılmaz, L. (2018). A Deep Learning-CNN Based System for Medical Diagnosis: An Application on Parkinson’s Disease Handwriting Drawings. In 2018 6th International Conference on Control Engineering & Information Technology (CEIT)

  25. 25.

    Gunduz, H. (2019). Deep learning-based parkinson’s disease classification using vocal feature sets. IEEE Access, 7, 115540–115551.

    Article  Google Scholar 

  26. 26.

    Mohamed, G. S. (2016). Parkinson’s disease diagnosis: Detecting the effect of attributes selection and discretization of parkinson’s disease dataset on the performance of classifier algorithms. Open Access Library Journal, 3(11), 1–11.

    Google Scholar 

  27. 27.

    Zwartjes, D. G., et al. (2010). Ambulatory monitoring of activities and motor symptoms in Parkinson’s disease. IEEE Transactions on Biomedical Engineering, 57(11), 778–2786.

    Article  Google Scholar 

  28. 28.

    Rigas, G., et al. (2012). Assessment of tremor activity in the Parkinson’s disease using a set of wearable sensors. IEEE Transactions on Information Technology in Biomedicine, 16(3), 478–487.

    Article  Google Scholar 

  29. 29.

    Cancela, J. (2010). A comprehensive motor symptom monitoring and management system: the bradykinesia case. In 2010 Annual International Conference of the IEEE Engineering in Medicine and Biology

  30. 30.

    Arrigo, A., et al. (2017). Visual system involvement in patients with newly diagnosed Parkinson disease. Radiology, 285(3), 885–895.

    Article  Google Scholar 

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Correspondence to Sajad Mohamadzadeh.

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Mohamadzadeh, S., Pasban, S., Zeraatkar-Moghadam, J. et al. Parkinson’s Disease Detection by Using Feature Selection and Sparse Representation. J. Med. Biol. Eng. (2021).

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  • Parkinson’s disease
  • Deep learning
  • Feature selection
  • Sparse representation
  • Learning
  • Speech attribute