Estimation of forced vital capacity using speech acoustics in patients with ALS

Gabriela M. Stegmann, Shira Hahn, Cayla J. Duncan, Seward B. Rutkove, Julie Liss, Jeremy M. Shefner, Visar Berisha

Research output: Contribution to journalArticlepeer-review

Abstract

In this study, we present and provide validation data for a tool that predicts forced vital capacity (FVC) from speech acoustics collected remotely via a mobile app without the need for any additional equipment (e.g. a spirometer). We trained a machine learning model on a sample of healthy participants and participants with amyotrophic lateral sclerosis (ALS) to learn a mapping from speech acoustics to FVC and used this model to predict FVC values in a new sample from a different study of participants with ALS. We further evaluated the cross-sectional accuracy of the model and its sensitivity to within-subject change in FVC. We found that the predicted and observed FVC values in the test sample had a correlation coefficient of.80 and mean absolute error between.54 L and.58 L (18.5% to 19.5%). In addition, we found that the model was able to detect longitudinal decline in FVC in the test sample, although to a lesser extent than the observed FVC values measured using a spirometer, and was highly repeatable (ICC = 0.92–0.94), although to a lesser extent than the actual FVC (ICC =.97). These results suggest that sustained phonation may be a useful surrogate for VC in both research and clinical environments.

Original languageEnglish (US)
Pages (from-to)14-21
Number of pages8
JournalAmyotrophic Lateral Sclerosis and Frontotemporal Degeneration
Volume22
Issue numberS1
DOIs
StatePublished - 2021

Keywords

  • biomarker
  • Clinical trial
  • respiratory function
  • speech analysis
  • ventilation

ASJC Scopus subject areas

  • Neurology
  • Clinical Neurology

Fingerprint

Dive into the research topics of 'Estimation of forced vital capacity using speech acoustics in patients with ALS'. Together they form a unique fingerprint.

Cite this