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International Conference on Smart Objects and Technologies for Social Good

GOODTECHS 2017: Smart Objects and Technologies for Social Good pp 206–215Cite as

Parkinson’s Disease Detection from Speech Using Convolutional Neural Networks

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Abstract

Application of deep learning tends to outperform hand-crafted features in many domains. This study uses convolutional neural networks to explore effectiveness of various segments of a speech signal, – text-dependent pronunciation of a short sentence, – in Parkinson’s disease detection task. Besides the common Mel-frequency spectrogram and its first and second derivatives, inclusion of various other input feature maps is also considered. Image interpolation is investigated as a solution to obtain a spectrogram of fixed length. The equal error rate (EER) for sentence segments varied from 20.3% to 29.5%. Fusion of decisions from sentence segments achieved EER of 14.1%, whereas the best result when using the full sentence exhibited EER of 16.8%. Therefore, splitting speech into segments could be recommended for Parkinson’s disease detection.

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Acknowledgements

Funding for this work was provided by a grant (No. MIP-075/2015) from the Research Council of Lithuania. The dataset was collected by the Department of Otorhinolaryngology at Lithuanian University of Health Sciences.

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Authors and Affiliations

  1. Department of Electrical Power Systems, Kaunas University of Technology, Studentu 50, 51368, Kaunas, Lithuania

    Evaldas Vaiciukynas, Adas Gelzinis, Antanas Verikas & Marija Bacauskiene

  2. Department of Information Systems, Kaunas University of Technology, Studentu 50, 51368, Kaunas, Lithuania

    Evaldas Vaiciukynas

  3. Centre for Applied Intelligent Systems Research, Halmstad University, Kristian IV:s väg 3, PO Box 823, 30118, Halmstad, Sweden

    Antanas Verikas

Authors
  1. Evaldas Vaiciukynas
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  2. Adas Gelzinis
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  3. Antanas Verikas
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  4. Marija Bacauskiene
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Corresponding author

Correspondence to Evaldas Vaiciukynas .

Editor information

Editors and Affiliations

  1. University of Pisa, Pisa, Italy

    Barbara Guidi

  2. University of Pisa, Pisa, Italy

    Laura Ricci

  3. Polytechnic University of Valencia, Valencia, Spain

    Carlos Calafate

  4. University of Padua, Padua, Italy

    Ombretta Gaggi

  5. University of Antwerp, Antwerp, Belgium

    Johann Marquez-Barja

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© 2018 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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Vaiciukynas, E., Gelzinis, A., Verikas, A., Bacauskiene, M. (2018). Parkinson’s Disease Detection from Speech Using Convolutional Neural Networks. In: Guidi, B., Ricci, L., Calafate, C., Gaggi, O., Marquez-Barja, J. (eds) Smart Objects and Technologies for Social Good. GOODTECHS 2017. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 233. Springer, Cham. https://doi.org/10.1007/978-3-319-76111-4_21

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  • DOI: https://doi.org/10.1007/978-3-319-76111-4_21

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-76110-7

  • Online ISBN: 978-3-319-76111-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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