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Automatic subtyping of individuals with Primary Progressive Aphasia

View ORCID ProfileCharalambos Themistocleous, Bronte Ficek, View ORCID ProfileKimberly Webster, Dirk-Bart den Ouden, View ORCID ProfileArgye E. Hillis, View ORCID ProfileKyrana Tsapkini
doi: https://doi.org/10.1101/2020.04.04.025593
Charalambos Themistocleous
1Johns Hopkins School of Medicine, Baltimore, MD 21287, USA
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  • For correspondence: cthemis1@jhu.edu
Bronte Ficek
1Johns Hopkins School of Medicine, Baltimore, MD 21287, USA
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Kimberly Webster
1Johns Hopkins School of Medicine, Baltimore, MD 21287, USA
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Dirk-Bart den Ouden
2Arnold School of Public Health, University of South Carolina, Columbia, SC, USA
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Argye E. Hillis
1Johns Hopkins School of Medicine, Baltimore, MD 21287, USA
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Kyrana Tsapkini
1Johns Hopkins School of Medicine, Baltimore, MD 21287, USA
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Abstract

Background The classification of patients with Primary Progressive Aphasia (PPA) into variants is time-consuming, costly, and requires combined expertise by clinical neurologists, neuropsychologists, speech pathologists, and radiologists.

Objective The aim of the present study is to determine whether acoustic and linguistic variables provide accurate classification of PPA patients into one of three variants: nonfluent PPA, semantic PPA, and logopenic PPA.

Methods In this paper, we present a machine learning model based on Deep Neural Networks (DNN) for the subtyping of patients with PPA into three main variants, using combined acoustic and linguistic information elicited automatically via acoustic and linguistic analysis. The performance of the DNN was compared to the classification accuracy of Random Forests, Support Vector Machines, and Decision Trees, as well as expert clinicians’ classifications.

Results The DNN model outperformed the other machine learning models with 80% classification accuracy, providing reliable subtyping of patients with PPA into variants and it even outperformed auditory classification of patients into variants by clinicians.

Conclusions We show that the combined speech and language markers from connected speech productions provide information about symptoms and variant subtyping in PPA. The end-to-end automated machine learning approach we present can enable clinicians and researchers to provide an easy, quick and inexpensive classification of patients with PPA.

Competing Interest Statement

The authors have declared no competing interest.

Footnotes

  • Publication history: This manuscript was previously published in bioRxiv:https://biorxiv.org/cgi/content/short/2020.04.04.025593v1

  • Disclosures: All of the authors report no disclosures.

  • We moved the description of the Neural Network from the Methodology into the Appendix to facilitate readability.

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. All rights reserved. No reuse allowed without permission.
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Posted June 28, 2020.
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Automatic subtyping of individuals with Primary Progressive Aphasia
Charalambos Themistocleous, Bronte Ficek, Kimberly Webster, Dirk-Bart den Ouden, Argye E. Hillis, Kyrana Tsapkini
bioRxiv 2020.04.04.025593; doi: https://doi.org/10.1101/2020.04.04.025593
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Automatic subtyping of individuals with Primary Progressive Aphasia
Charalambos Themistocleous, Bronte Ficek, Kimberly Webster, Dirk-Bart den Ouden, Argye E. Hillis, Kyrana Tsapkini
bioRxiv 2020.04.04.025593; doi: https://doi.org/10.1101/2020.04.04.025593

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