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Identification of Parkinson’s Disease from Speech Using CNNs and Formant Measures

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Artificial Intelligence in Neuroscience: Affective Analysis and Health Applications (IWINAC 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13258))

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Abstract

Parkinson’s Disease (PD) is a neurodegenerative disorder that severely impacts the motor capabilities of patients. Dysarthria is one of the symptoms that can be accurately characterized using speech analysis, tracking the deterioration associated with the evolution of the disease. Through the present work the use of machine learning-based technologies, more specifically the Convolutional Neural Networks (CNNs) and the direct application of formant features extracted form sustained phonations of vowel /a/ are proposed. The main goal is to investigate the effects of the speech articulatory movements affected by hypokinetic dysarthria in Parkinson’s Disease as this would allow to use speech as a reliable monitoring tool. The study employs voice recording of 593 subjects form the Patient Voice Analysis dataset (PVA) and 687 health controls from the Saarbrücken Voice Database (SVD). The k-fold cross-validation trials provided the best results when the length of the utterances is limited to 2 s, achieving a sensibility of 0.96 and a specificity of 0.99.

This research received funding from grants TEC2016-77791-C4-4-R (Ministry of Economic Affairs and Competitiveness of Spain), and Teca-Park-MonParLoc FGCSICCENIE 0348-CIE-6-E (InterReg Programme). PVA datasets were generated through collaboration between Sage Bionetworks, PatientsLikeMe and Dr. Max Little as part of the Patient Voice Analysis study (PVA). They were obtained through Synapse ID [syn2321745].

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Authors and Affiliations

  1. NeuSpeLab, CTB, Universidad Politécnica de Madrid, 28223, Pozuelo de Alarcón, Madrid, Spain

    Agustín Álvarez-Marquina & Pedro Gómez-Vilda

  2. Faculty of Medicine, Usher Institute, University of Edinburgh, Edinburgh, UK

    Andrés Gómez-Rodellar

  3. E.T.S. de Ingeniería Informática - Universidad Rey Juan Carlos, Campus de Móstoles, Tulipán, s/n, 28933, Móstoles, Madrid, Spain

    Daniel Palacios-Alonso

  4. E.T.S. de Ingeniería de Sistemas Informáticos - Universidad Politécnica de Madrid, Campus Sur, Alang Turing, s/n, 28031, Madrid, Spain

    Francisco Díaz-Pérez

Authors
  1. Agustín Álvarez-Marquina
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  2. Andrés Gómez-Rodellar
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  3. Pedro Gómez-Vilda
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  4. Daniel Palacios-Alonso
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  5. Francisco Díaz-Pérez
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Correspondence to Agustín Álvarez-Marquina .

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Editors and Affiliations

  1. Universidad Politécnica de Cartagena, Cartagena, Spain

    José Manuel Ferrández Vicente

  2. Universidad Nacional de Educación a Distancia, Madrid, Spain

    José Ramón Álvarez-Sánchez

  3. Universidad Nacional de Educación a Distancia, Madrid, Spain

    Félix de la Paz López

  4. Ohio State University, Columbus, OH, USA

    Hojjat Adeli

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Álvarez-Marquina, A., Gómez-Rodellar, A., Gómez-Vilda, P., Palacios-Alonso, D., Díaz-Pérez, F. (2022). Identification of Parkinson’s Disease from Speech Using CNNs and Formant Measures. In: Ferrández Vicente, J.M., Álvarez-Sánchez, J.R., de la Paz López, F., Adeli, H. (eds) Artificial Intelligence in Neuroscience: Affective Analysis and Health Applications. IWINAC 2022. Lecture Notes in Computer Science, vol 13258. Springer, Cham. https://doi.org/10.1007/978-3-031-06242-1_33

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  • DOI: https://doi.org/10.1007/978-3-031-06242-1_33

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-06241-4

  • Online ISBN: 978-3-031-06242-1

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