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Comparison of CNN-Learned vs. Handcrafted Features for Detection of Parkinson's Disease Dysgraphia in a Multilingual Dataset

The result's identifiers

  • Result code in IS VaVaI

    <a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00159816%3A_____%2F22%3A00077932" target="_blank" >RIV/00159816:_____/22:00077932 - isvavai.cz</a>

  • Alternative codes found

    RIV/00216305:26220/22:PU144605 RIV/00216224:14740/22:00129679

  • Result on the web

    <a href="https://www.frontiersin.org/articles/10.3389/fninf.2022.877139/full" target="_blank" >https://www.frontiersin.org/articles/10.3389/fninf.2022.877139/full</a>

  • DOI - Digital Object Identifier

    <a href="http://dx.doi.org/10.3389/fninf.2022.877139" target="_blank" >10.3389/fninf.2022.877139</a>

Alternative languages

  • Result language

    angličtina

  • Original language name

    Comparison of CNN-Learned vs. Handcrafted Features for Detection of Parkinson's Disease Dysgraphia in a Multilingual Dataset

  • Original language description

    Parkinson&apos;s disease dysgraphia (PDYS), one of the earliest signs of Parkinson&apos;s disease (PD), has been researched as a promising biomarker of PD and as the target of a noninvasive and inexpensive approach to monitoring the progress of the disease. However, although several approaches to supportive PDYS diagnosis have been proposed (mainly based on handcrafted features (HF) extracted from online handwriting or the utilization of deep neural networks), it remains unclear which approach provides the highest discrimination power and how these approaches can be transferred between different datasets and languages. This study aims to compare classification performance based on two types of features: features automatically extracted by a pretrained convolutional neural network (CNN) and HF designed by human experts. Both approaches are evaluated on a multilingual dataset collected from 143 PD patients and 151 healthy controls in the Czech Republic, United States, Colombia, and Hungary. The subjects performed the spiral drawing task (SDT; a language-independent task) and the sentence writing task (SWT; a language-dependent task). Models based on logistic regression and gradient boosting were trained in several scenarios, specifically single language (SL), leave one language out (LOLO), and all languages combined (ALC). We found that the HF slightly outperformed the CNN-extracted features in all considered evaluation scenarios for the SWT. In detail, the following balanced accuracy (BACC) scores were achieved: SL-0.65 (HF), 0.58 (CNN); LOLO-0.65 (HF), 0.57 (CNN); and ALC-0.69 (HF), 0.66 (CNN). However, in the case of the SDT, features extracted by a CNN provided competitive results: SL-0.66 (HF), 0.62 (CNN); LOLO-0.56 (HF), 0.54 (CNN); and ALC-0.60 (HF), 0.60 (CNN). In summary, regarding the SWT, the HF outperformed the CNN-extracted features over 6% (mean BACC of 0.66 for HF, and 0.60 for CNN). In the case of the SDT, both feature sets provided almost identical classification performance (mean BACC of 0.60 for HF, and 0.58 for CNN).

  • Czech name

  • Czech description

Classification

  • Type

    J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database

  • CEP classification

  • OECD FORD branch

    30103 - Neurosciences (including psychophysiology)

Result continuities

  • Project

    <a href="/en/project/NU20-04-00294" target="_blank" >NU20-04-00294: Diagnostics of Lewy body diseases in prodromal stage based on multimodal data analysis</a><br>

  • Continuities

    I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Others

  • Publication year

    2022

  • Confidentiality

    S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Data specific for result type

  • Name of the periodical

    Frontiers in Neuroinformatics

  • ISSN

    1662-5196

  • e-ISSN

  • Volume of the periodical

    16

  • Issue of the periodical within the volume

    MAY 30

  • Country of publishing house

    CH - SWITZERLAND

  • Number of pages

    18

  • Pages from-to

    877139

  • UT code for WoS article

    000811334000001

  • EID of the result in the Scopus database