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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))

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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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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