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Detecting Parkinson’s Disease According to Gender Using Speech Signals

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Knowledge Science, Engineering and Management (KSEM 2021)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 12817))

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Abstract

Parkinson’s Disease (PD) is a progressive neurodegenerative disorder that mainly affects the central nervous system causing cognitive, emotional and language disorders. Speech impairment is one of the earliest PD symptoms, and may be used for an automatic assessment to support the diagnosis and the evaluation of the disease severity, in the two biological sexes (male and female). This study investigates the processing of voice signals for measuring the incidence of Parkinson’s disease in women and men. The approach evaluates the use of several extracted features and two learning techniques Support Vector Machines (SVM) and Long Short-Term Memory (LSTM) to classify data obtained from four databases. Each database contains different data to each other and in a different language. The audio tasks were recorded using six different microphone. The results reveal cases of Parkinson’s disease appear more in men than in women.

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References

  1. von Campenhausen, S.: Costs of illness and care in Parkinson’s disease: an evaluation in six countries. Eur. Neuropsychopharmacol. 21, 180–191 (2011)

    Article  Google Scholar 

  2. Goetz Christopher, G.: The history of Parkinson’s disease: early clinical descriptions and neurological therapies. Cold Spring Harbor Perspect. Med. 1 (2011)

    Google Scholar 

  3. Smith, P.D.: Cyclin-dependent kinase 5 is a mediator of dopaminergic neuron loss in a mouse model of Parkinson’s disease. Proc. Natl. Acad. Sci. 100, 13650–13655 (2003)

    Article  Google Scholar 

  4. Pinto, S.: La dysarthrie au cours de la maladie de Parkinson. Histoire naturelle de ses composantes: dysphonie, dysprosodie et dysarthrie. Revue neurologique 166, 800–810 (2010)

    Google Scholar 

  5. Aileen, H.: Speech impairment in a large sample of patients with Parkinson’s disease. Behav. Neurol. 11, 131–137 (1998)

    Google Scholar 

  6. Upadhya, S.S.: Discriminating Parkinson and healthy people using phonation and cepstral features of speech. Procedia Comput. Sci. 143, 197–202 (2018)

    Article  Google Scholar 

  7. Upadhya Savitha, S.: Statistical comparison of jitter and shimmer voice features for healthy and Parkinson affected persons. In: Second International Conference on Electrical, Computer and Communication Technologies (ICECCT), pp. 1–6 (2017)

    Google Scholar 

  8. Taha, K.: Classification of speech intelligibility in Parkinson’s disease. Biocybern. Biomed. Eng. 34, 35–45 (2014)

    Article  Google Scholar 

  9. Mohammad, S.: Speech analysis for diagnosis of Parkinson’s disease using genetic algorithm and support vector machine. J. Biomed. Sci. Eng. 7, 147–156 (2014)

    Article  Google Scholar 

  10. Proença, J., Veiga, A., Candeias, S., Lemos, J., Januário, C., Perdigão, F.: Characterizing Parkinson’s disease speech by acoustic and phonetic features. In: Baptista, J., Mamede, N., Candeias, S., Paraboni, I., Pardo, T.A.S., Volpe Nunes, M.G. (eds.) PROPOR 2014. LNCS (LNAI), vol. 8775, pp. 24–35. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-09761-9_3

    Chapter  Google Scholar 

  11. Mehmet, C.: Automatic recognition of Parkinson’s disease from sustained phonation tests using ANN and adaptive neuro-fuzzy classifier. J. Eng. Sci. Des. 1, 59–64 (2010)

    Google Scholar 

  12. David, M.: A Diadochokinesis-based expert system considering articulatory features of plosive consonants for early detection of Parkinson’s disease. Comput. Methods Programs Biomed. 154, 89–97 (2018)

    Article  Google Scholar 

  13. Diogo, B.: Automatic detection of Parkinson’s disease based on acoustic analysis of speech. Eng. Appl. Artif. Intell. 77, 148–158 (2019)

    Article  Google Scholar 

  14. Biswajit, K.: Parkinson disease prediction using intrinsic mode function based features from speech signal. Biocybern. Biomed. Eng. 40, 249–264 (2020)

    Article  Google Scholar 

  15. Takashi, T.: Clinical correlates of repetitive speech disorders in Parkinson’s disease. J. Neurol. Sci. 401, 67–71 (2019)

    Article  Google Scholar 

  16. Sotirios, P.: Speech difficulties in early de novo patients with Parkinson’s disease. Parkinsonism Relat. Disord. 64, 256–261 (2019)

    Article  Google Scholar 

  17. Betul, S.: Collection and analysis of a Parkinson speech dataset with multiple types of sound recordings. IEEE J. Biomed. Health Inform. 17, 828–834 (2013)

    Article  Google Scholar 

  18. Khaskhoussy, R., Ayed, Y.B.: Automatic detection of Parkinson’s disease from speech using acoustic, prosodic and phonetic features. In: Abraham, A., Siarry, P., Ma, K., Kaklauskas, A. (eds.) ISDA 2019. AISC, vol. 1181, pp. 80–89. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-49342-4_8

    Chapter  Google Scholar 

  19. Evaldas, V.: Detecting Parkinson’s disease from sustained phonation and speech signals. PLoS ONE 12, 1–16 (2017)

    Google Scholar 

  20. Fahn, S.: Unified Parkinson’s disease rating scale. Recent developments in Parkinson’s disease volume II. Macmillan Healthcare Inf. 2, 153–163 (1987)

    Google Scholar 

  21. Bernhard, B.: A training algorithm for optimal margin classifiers. In: Proceedings of the Fifth Annual Workshop on Computational Learning Theory, pp. 144–152 (1992)

    Google Scholar 

  22. Frank, D.: Recognizing emotion in speech. In: Proceeding of Fourth International Conference on Spoken Language Processing, ICSLP 1996, vol. 3, pp. 1970–1973 (1996)

    Google Scholar 

  23. Saloni, R.K.: Detection of Parkinson disease using clinical voice data mining. Int. J. Circuits Syst. Signal Process. 9, 320–326 (2015)

    Google Scholar 

  24. Sepp, H.: Long short-term memory. Neural Comput. 9, 1735–1780 (1997)

    Article  Google Scholar 

  25. Huang, C.-W.: Attention assisted discovery of sub-utterance structure in speech emotion recognition. In: INTERSPEECH, pp. 1387–1391 (2016)

    Google Scholar 

  26. Miller, I.N.: Gender differences in Parkinson’s disease: clinical characteristics and cognition. Mov. Disord. 25, 2695–2703 (2010)

    Article  Google Scholar 

  27. Gillies, G.E.: Sex differences in Parkinson’s disease. Front. Neuroendocrinol. 35, 370–384 (2014)

    Article  Google Scholar 

  28. Podcasy, J.L.: Considering sex and gender in Alzheimer disease and other dementias. Dialogues Clin. Neurosci. 18, 437 (2016)

    Article  Google Scholar 

  29. Gai, K.: Reinforcement learning-based content-centric services in mobile sensing. IEEE Netw. 32, 34–39 (2018)

    Article  Google Scholar 

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Author information

Authors and Affiliations

  1. MIRACL: Multimedia Information System and Advanced Computing Laboratory, Sfax University, Sfax, Tunisia

    Rania Khaskhoussy & Yassine Ben Ayed

Authors
  1. Rania Khaskhoussy
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  2. Yassine Ben Ayed
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Corresponding author

Correspondence to Rania Khaskhoussy .

Editor information

Editors and Affiliations

  1. Tsinghua University, Beijing, China

    Han Qiu

  2. Ibaraki University, Hitachi, Japan

    Cheng Zhang

  3. University of Kentucky, Lexington, KY, USA

    Zongming Fei

  4. Texas A&M University – Commerce, Commerce, TX, USA

    Meikang Qiu

  5. Princeton University, Princeton, NJ, USA

    Sun-Yuan Kung

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Khaskhoussy, R., Ayed, Y.B. (2021). Detecting Parkinson’s Disease According to Gender Using Speech Signals. In: Qiu, H., Zhang, C., Fei, Z., Qiu, M., Kung, SY. (eds) Knowledge Science, Engineering and Management. KSEM 2021. Lecture Notes in Computer Science(), vol 12817. Springer, Cham. https://doi.org/10.1007/978-3-030-82153-1_34

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  • DOI: https://doi.org/10.1007/978-3-030-82153-1_34

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-82152-4

  • Online ISBN: 978-3-030-82153-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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