IDEAS home Printed from https://ideas.repec.org/a/eee/phsmap/v471y2017icp98-105.html
   My bibliography  Save this article

Parkinson’s disease detection based on dysphonia measurements

Author

Listed:
  • Lahmiri, Salim

Abstract

Assessing dysphonic symptoms is a noninvasive and effective approach to detect Parkinson’s disease (PD) in patients. The main purpose of this study is to investigate the effect of different dysphonia measurements on PD detection by support vector machine (SVM). Seven categories of dysphonia measurements are considered. Experimental results from ten-fold cross-validation technique demonstrate that vocal fundamental frequency statistics yield the highest accuracy of 88%±0.04. When all dysphonia measurements are employed, the SVM classifier achieves 94%±0.03 accuracy. A refinement of the original patterns space by removing dysphonia measurements with similar variation across healthy and PD subjects allows achieving 97.03%±0.03 accuracy. The latter performance is larger than what is reported in the literature on the same dataset with ten-fold cross-validation technique. Finally, it was found that measures of ratio of noise to tonal components in the voice are the most suitable dysphonic symptoms to detect PD subjects as they achieve 99.64%±0.01 specificity. This finding is highly promising for understanding PD symptoms.

Suggested Citation

  • Lahmiri, Salim, 2017. "Parkinson’s disease detection based on dysphonia measurements," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 471(C), pages 98-105.
    • Handle: RePEc:eee:phsmap:v:471:y:2017:i:c:p:98-105
    DOI: 10.1016/j.physa.2016.12.009
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0378437116309906
    Download Restriction: Full text for ScienceDirect subscribers only. Journal offers the option of making the article available online on Science direct for a fee of $3,000
    File URL: https://libkey.io/10.1016/j.physa.2016.12.009?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Wang, Min & Wang, Bei & Zou, Junzhong & Nakamura, Masatoshi, 2012. "A new quantitative evaluation method of spiral drawing for patients with Parkinson’s disease based on a polar coordinate system with varying origin," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(18), pages 4377-4388.
    2. Yulmetyev, Renat & Demin, Sergey & Emelyanova, Natalya & Gafarov, Fail & Hänggi, Peter, 2003. "Stratification of the phase clouds and statistical effects of the non-Markovity in chaotic time series of human gait for healthy people and Parkinson patients," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 319(C), pages 432-446.
    3. Gozolchiani, Avi & Moshel, Shay & Hausdorff, Jeffrey M. & Simon, Ely & Kurths, Jürgen & Havlin, Shlomo, 2006. "Decaying of phase synchronization in parkinsonian tremor," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 366(C), pages 552-560.
    4. Blesić, S. & Stratimirović, Dj. & Milošević, S. & Marić, J. & Kostić, V. & Ljubisavljević, M., 2011. "Scaling analysis of the effects of load on hand tremor movements in essential tremor," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 390(10), pages 1741-1746.
    5. Yulmetyev, R.M. & Demin, S.A. & Panischev, O. Yu. & Hänggi, Peter & Timashev, S.F. & Vstovsky, G.V., 2006. "Regular and stochastic behavior of Parkinsonian pathological tremor signals," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 369(2), pages 655-678.
    6. Figueiredo, Thiago C. & Vivas, Jamile & Peña, Norberto & Miranda, José G.V., 2016. "Fractal measures of video-recorded trajectories can classify motor subtypes in Parkinson’s Disease," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 462(C), pages 12-20.
    7. Dutta, Srimonti & Ghosh, Dipak & Samanta, Shukla, 2016. "Non linear approach to study the dynamics of neurodegenerative diseases by Multifractal Detrended Cross-correlation Analysis—A quantitative assessment on gait disease," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 448(C), pages 181-195.
    8. Lahmiri, Salim, 2016. "Image characterization by fractal descriptors in variational mode decomposition domain: Application to brain magnetic resonance," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 456(C), pages 235-243.
    9. Yang, Huijie & Zhao, Fangcui & Zhuo, Yizhong & Wu, Xizhen & Li, Zhuxia, 2002. "Investigation on gait time series by means of factorial moments," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 312(1), pages 23-34.
    10. Demin, S.A. & Yulmetyev, R.M. & Panischev, O.Yu. & Hänggi, Peter, 2008. "Statistical quantifiers of memory for an analysis of human brain and neuro-system diseases," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 387(8), pages 2100-2110.
    11. Echeverria, Juan C. & Rodriguez, Eduardo & Velasco, Alejandra & Alvarez-Ramirez, Jose, 2010. "Limb dominance changes in walking evolution explored by asymmetric correlations in gait dynamics," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 389(8), pages 1625-1634.
    12. Hausdorff, Jeffrey M & Balash, Y & Giladi, Nir, 2003. "Time series analysis of leg movements during freezing of gait in Parkinson's disease: akinesia, rhyme or reason?," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 321(3), pages 565-570.
    13. de Oliveira, M. Elias & Menegaldo, L.L. & Lucarelli, P. & Andrade, B.L.B. & Büchler, P., 2011. "On the use of information theory for detecting upper limb motor dysfunction: An application to Parkinson’s disease," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 390(23), pages 4451-4458.
    14. Yulmetyev, Renat M. & Yulmetyeva, Dinara & Gafarov, Fail M., 2005. "How chaosity and randomness control human health," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 354(C), pages 404-414.
    15. Bartsch, Ronny & Plotnik, Meir & Kantelhardt, Jan W. & Havlin, Shlomo & Giladi, Nir & Hausdorff, Jeffrey M., 2007. "Fluctuation and synchronization of gait intervals and gait force profiles distinguish stages of Parkinson's disease," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 383(2), pages 455-465.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Lahmiri, Salim & Bekiros, Stelios, 2022. "Complexity measures of high oscillations in phonocardiogram as biomarkers to distinguish between normal heart sound and pathological murmur," Chaos, Solitons & Fractals, Elsevier, vol. 154(C).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Lahmiri, Salim, 2018. "Generalized Hurst exponent estimates differentiate EEG signals of healthy and epileptic patients," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 490(C), pages 378-385.
    2. de Oliveira, M. Elias & Menegaldo, L.L. & Lucarelli, P. & Andrade, B.L.B. & Büchler, P., 2011. "On the use of information theory for detecting upper limb motor dysfunction: An application to Parkinson’s disease," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 390(23), pages 4451-4458.
    3. Yang, Shuangming & Wei, Xile & Deng, Bin & Liu, Chen & Li, Huiyan & Wang, Jiang, 2018. "Efficient digital implementation of a conductance-based globus pallidus neuron and the dynamics analysis," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 494(C), pages 484-502.
    4. Yulmetyev, R.M. & Demin, S.A. & Panischev, O. Yu. & Hänggi, Peter & Timashev, S.F. & Vstovsky, G.V., 2006. "Regular and stochastic behavior of Parkinsonian pathological tremor signals," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 369(2), pages 655-678.
    5. De Gregorio, Juan & Sánchez, David & Toral, Raúl, 2022. "An improved estimator of Shannon entropy with applications to systems with memory," Chaos, Solitons & Fractals, Elsevier, vol. 165(P1).
    6. E. M. S. Ribeiro & G. A. Prataviera, 2014. "Information theoretic approach for accounting classification," Papers 1401.2954, arXiv.org, revised Sep 2014.
    7. Zhang, Hong-Yan & Kang, Ming-Cui & Li, Jing-Qiang & Liu, Hai-Tao, 2017. "R/S analysis of reaction time in Neuron Type Test for human activity in civil aviation," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 469(C), pages 859-870.
    8. Segun Goh & Kyungreem Han & Jehkwang Ryu & Seonjin Kim & MooYoung Choi, 2015. "Failure of Arm Movement Control in Stroke Patients, Characterized by Loss of Complexity," PLOS ONE, Public Library of Science, vol. 10(11), pages 1-17, November.
    9. Yulmetyev, Renat M. & Demin, Sergey A. & Panischev, Oleg Yu. & Hänggi, Peter, 2005. "Age-related alterations of relaxation processes and non-Markov effects in stochastic dynamics of R–R intervals variability from human ECGs," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 353(C), pages 336-352.
    10. Kembro, Jackelyn M. & Flesia, Ana Georgina & Gleiser, Raquel M. & Perillo, María A. & Marin, Raul H., 2013. "Assessment of long-range correlation in animal behavior time series: The temporal pattern of locomotor activity of Japanese quail (Coturnix coturnix) and mosquito larva (Culex quinquefasciatus)," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(24), pages 6400-6413.
    11. Du, Pei & Wang, Jianzhou & Yang, Wendong & Niu, Tong, 2020. "Point and interval forecasting for metal prices based on variational mode decomposition and an optimized outlier-robust extreme learning machine," Resources Policy, Elsevier, vol. 69(C).
    12. Gupta, Aparna & Li, Zhisheng, 2011. "Calibration of a stochastic health evolution model using NHIS data," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 390(20), pages 3524-3540.
    13. Ribeiro, E.M.S. & Prataviera, G.A., 2014. "Information theoretic approach for accounting classification," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 416(C), pages 651-660.
    14. Lahmiri, Salim, 2017. "Multifractal analysis of Moroccan family business stock returns," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 486(C), pages 183-191.
    15. Pablo Arias & Javier Cudeiro, 2010. "Effect of Rhythmic Auditory Stimulation on Gait in Parkinsonian Patients with and without Freezing of Gait," PLOS ONE, Public Library of Science, vol. 5(3), pages 1-8, March.
    16. Mulligan, Robert F., 2014. "Multifractality of sectoral price indices: Hurst signature analysis of Cantillon effects in disequilibrium factor markets," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 403(C), pages 252-264.
    17. Kar, Alpa & Chatterjee, Sucharita & Ghosh, Dipak, 2019. "Multifractal detrended cross correlation analysis of Land-surface temperature anomalies and Soil radon concentration," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 521(C), pages 236-247.
    18. Marin-Lopez, A. & Martínez-Cadena, J.A. & Martinez-Martinez, F. & Alvarez-Ramirez, J., 2023. "Surrogate multivariate Hurst exponent analysis of gait dynamics," Chaos, Solitons & Fractals, Elsevier, vol. 172(C).
    19. Salim Lahmiri, 2016. "Features selection, data mining and finacial risk classification: a comparative study," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 23(4), pages 265-275, October.
    20. Alvarez-Ramirez, J. & Echeverria, J.C. & Meraz, M. & Rodriguez, E., 2017. "Asymmetric acceleration/deceleration dynamics in heart rate variability," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 479(C), pages 213-224.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:phsmap:v:471:y:2017:i:c:p:98-105. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/physica-a-statistical-mechpplications/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.