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An electroglottographical analysis-based discriminant function model differentiating multiple sclerosis patients from

George D Vavougios1,2, Triantafyllos Doskas1,2, Kostas Konstantopoulos3,4

  • 1University of Thessaly, Biopolis, 41110, Larissa, Greece.

Neurological Sciences : Official Journal of the Italian Neurological Society and of the Italian Society of Clinical Neurophysiology
|February 17, 2018
PubMed
Summary

Electroglottography can differentiate patients with multiple sclerosis (MS) from healthy individuals. This voice analysis method achieves 100% accuracy in classifying MS patients, offering a potential new biomarker.

Keywords:
ElectroglottographyLinear discriminant function analysisMultiple sclerosis

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Area of Science:

  • Neurology
  • Speech-Language Pathology
  • Biomedical Engineering

Background:

  • Dysarthrophonia, a speech disorder, significantly impacts the quality of life for individuals with neurological diseases.
  • Previous research has explored various methods to assess speech impairments in neurological conditions.

Purpose of the Study:

  • To develop a discriminant function equation using novel electroglottographic variables to distinguish multiple sclerosis (MS) patients from healthy controls.
  • To evaluate the diagnostic accuracy of this electroglottographic model.

Main Methods:

  • Stepwise linear discriminant function analysis (DFA) was applied to electroglottographic data.
  • A discriminant function was derived using specific variables from monologue and reading speech tasks.
  • A 2x2 confusion matrix and leave-one-out cross-validation were used to assess predictive accuracy.

Main Results:

  • A statistically significant discriminant function was developed (Wilk's λ = 0.043, p < 0.0001).
  • The derived model achieved 100% sensitivity and 100% specificity with a cutoff score of -0.788.
  • Electroglottography demonstrated high classification accuracy for MS patients.

Conclusions:

  • Electroglottographic evaluation is a simple and effective method for classifying MS patients.
  • The findings suggest electroglottography's potential as a valuable biomarker for MS.
  • Further research is warranted to establish its clinical utility as a biomarker.