Estimation of forced vital capacity using speech acoustics in patients with ALS. (30th July 2021)

• Record Type: Journal Article
• Authors: Stegmann, Gabriela M.; Hahn, Shira; Duncan, Cayla J.; Rutkove, Seward B.; Liss, Julie; Shefner, Jeremy M.; Berisha, Visar
• Is Part Of: Amyotrophic lateral sclerosis and frontotemporal degeneration. Volume 22(2021)Supplement 1
• Page Start: 14
• Page End: 21
• Publication Date: 2021-07-30
• Subjects: Clinical trial -- respiratory function -- speech analysis -- biomarker -- ventilation; 616.839
• DOI: 10.1080/21678421.2020.1866013
• Languages: English
• ISSNs: 2167-8421

Abstract

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.