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Enhancing the Classification of Aphasia: A Statistical Analysis Using Connected Speech.

Davida Fromm1, Joel Greenhouse2, Mitchell Pudil2

  • 1Department of Psychology, Carnegie Mellon University.

Aphasiology
|December 2, 2022
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Summary
This summary is machine-generated.

Automated analysis of connected speech in people with aphasia (PWA) identified seven distinct clusters. Key features like total words and closed-class words effectively distinguished these PWA groups.

Keywords:
aphasiaclassificationclustering methodsdiscourse

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Area of Science:

  • Computational linguistics
  • Neuroscience
  • Speech-language pathology

Background:

  • Large databases and automated language analysis offer novel methods for studying connected speech in people with aphasia (PWA).
  • These techniques can provide new insights into the linguistic characteristics of PWA.

Purpose of the Study:

  • To group people with aphasia (PWA) into coherent clusters based on their language output using unsupervised statistical methods.
  • To identify specific linguistic features most strongly correlated with these identified clusters.

Main Methods:

  • Applied K-means clustering and random forests classification algorithms to language production data from 168 PWA.
  • Utilized language samples from a standard discourse protocol covering four genres: free speech, personal narratives, picture descriptions, Cinderella storytelling, and procedural discourse.

Main Results:

  • Identified seven distinct clusters of PWA using the K-means algorithm.
  • Developed and validated a random forests classification tree with 91% agreement with cluster assignments.
  • The most discriminative features were total words from free speech tasks and total closed-class words from the Cinderella storytelling task.

Conclusions:

  • Connected speech data effectively categorizes PWA into distinct, coherent groups.
  • Findings offer insights into traditional aphasia classifications and can inform future discourse research and clinical practice.