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Parkinson’s disease detection based on dysphonia measurements

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  • 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
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    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.
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    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).

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