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International Conference on Advanced Research in Technologies, Information, Innovation and Sustainability

ARTIIS 2022: Advanced Research in Technologies, Information, Innovation and Sustainability pp 58–70Cite as

VGG11 Parkinson’s Disease Detection Based on Voice Attributes

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Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1675))

Abstract

Parkinson disease is the second most world widespread neural impairment. It affects approximately 2 to 3% of world’s population with age over 65 years. Part of Parkinson’s disease progress happens due the loss of cells in a brain region called Substantia Nigra (SN). Nerve cells in this region are responsible for improving the control of movements and coordination. The loss of such cells results in the emerge of the motor symptoms characteristic of the disease. However, motor symptoms appear when brain cells are already damaged, while oppositely voice impairments appear before the brain cells are being affected. This study aims to recognize Parkinson disease using 22 attributes, extracted from 195 voice records, being 147 from Parkinson disease patients and 48 from healthy individuals. The data is passed through a series of pre-processing steps, being them: balancing, where we applied a Synthetic Minority Oversampling Technique (SMOTE) to make the number of data per class equal, training and test segmentation, where the data was divided in 30% for testing and 70% for training, scaling, the data into intervals of 0 to 1, amplification, step where the values are converted into intervals from 0 to 100 and image generation, converting the numerical dataset into an image dataset. Later, the resulted image dataset was used to train a Visual Geometry Group 11 (VGG11) Convolutional Neural Network (CNN). The proposed solution achieved 93.1% accuracy, 92.31% f1-score, 96% recall, 88.89% precision on testing dataset, displaying a good performance when compared with other explored solutions.

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Authors and Affiliations

  1. Kaunas University of Technology, K. Donelaičio St. 73, 44249, Kaunas, Lithuania

    Lucas Salvador Bernardo & Robertas Damaševičius

Authors
  1. Lucas Salvador Bernardo
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  2. Robertas Damaševičius
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Correspondence to Lucas Salvador Bernardo .

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Editors and Affiliations

  1. Universidad Estatal Península de Santa, La Libertad, Ecuador

    Teresa Guarda

  2. University of Minho, Guimarães, Portugal

    Filipe Portela

  3. BITrum Research Group, Leon, Spain

    Maria Fernanda Augusto

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Bernardo, L.S., Damaševičius, R. (2022). VGG11 Parkinson’s Disease Detection Based on Voice Attributes. In: Guarda, T., Portela, F., Augusto, M.F. (eds) Advanced Research in Technologies, Information, Innovation and Sustainability. ARTIIS 2022. Communications in Computer and Information Science, vol 1675. Springer, Cham. https://doi.org/10.1007/978-3-031-20319-0_5

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  • DOI: https://doi.org/10.1007/978-3-031-20319-0_5

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-20318-3

  • Online ISBN: 978-3-031-20319-0

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